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<h2 id="context">Context</h2> <p class="note">Main article: <a href="/facts/Strategy/vnQ3kcLw">Strategy</a></p> <p class="note">For broader coverage of this topic, see <a href="/facts/Trial_and_error/NGm2GoEd">Trial and error</a>, <a href="/facts/Rule_of_thumb/RopfGsJ0">Rule of thumb</a>, and <a href="/facts/Guessing/9Kq3CIym">Guessing</a>.</p> <p>Gigerenzer & Gaissmaier (2011) state that <a href="/facts/Set_(mathematics)/BfucfHMq">sub-sets</a> of <i>strategy</i> include heuristics, <a href="/facts/Regression_analysis/n6z5Tf7K">regression analysis</a>, and <a href="/facts/Bayesian_inference/tEpK3zLx">Bayesian inference</a>.<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> </p> <blockquote><p>A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a></p>— S. Chow, "Many Meanings of 'Heuristic'", <i>The British Journal for the Philosophy of Science</i></blockquote> <p>Heuristics are strategies based on rules to generate <a href="/facts/Optimal_decision/3qplyF1M">optimal decisions</a>, like the <a href="/facts/Anchoring_effect/V793WguQ">anchoring effect</a> and <a href="/facts/Utility_maximization_problem/pnD7dss4">utility maximization problem</a>.<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> These strategies depend on using readily accessible, though loosely applicable, information to control <a href="/facts/Problem_solving/9cuLz5pH">problem solving</a> in human beings, machines and abstract issues.<a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a><a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a> When an individual applies a heuristic in practice, it generally performs as expected. However it can alternatively create systematic errors.<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> </p><p>The most fundamental heuristic is trial and error, which can be used in everything from matching nuts and bolts to finding the values of variables in algebra problems. In mathematics, some common heuristics involve the use of visual representations, additional assumptions, forward/backward reasoning and simplification. </p><p><a href="/facts/Dual_process_theory/3Dqz1r0K">Dual process theory</a> concerns <a href="/facts/Embodied_cognition/zgewISTW">embodied heuristics</a>.<a class="footnote-ref" id="fnref:20" href="#fn:20"><sup>20</sup></a> </p> <h2 id="heuristic-rigour-models">Heuristic rigour models</h2> <p class="note">Main article: <a href="/facts/Rigour/v23FKfhw">Rigour</a></p> <p class="note">For broader coverage of this topic, see <a href="/facts/Expectation_(epistemic)/Llh1sS9a">Expectation (epistemic)</a> and <a href="/facts/Cybernetics/4qb58zbt">Cybernetics</a>.</p> <p class="note">See also: <a href="/facts/Heuristic_(computer_science)/PM2qkoa2">Heuristic (computer science)</a> and <a href="/facts/Heuristic_evaluation/1VzbMCmJ">Heuristic evaluation</a></p> <p>Lakatosian heuristics is based on the key term: <a href="/facts/Justification_(epistemology)/CxWaj0pY">Justification (epistemology)</a>.<a class="footnote-ref" id="fnref:21" href="#fn:21"><sup>21</sup></a> </p> <h3>One-reason decisions</h3> <p class="note">See also: <a href="/facts/Optimal_stopping/33AYC76Y">Optimal stopping</a></p> <p>One-reason decisions are <a href="/facts/Algorithm/fnl5NmRt">algorithms</a> that are made of three rules: search rules, <i>confirmation rules</i> (stopping), and decision rules<a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a><a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a><a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a> </p> <ul><li><a href="/facts/Take-the-best_heuristic/IcppA5Ih">Take-the-best heuristic</a> – Decision-making strategy<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a><a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a><a class="footnote-ref" id="fnref:27" href="#fn:27"><sup>27</sup></a></li> <li><i>Hiatus heuristic</i>: a "recency-of-last-purchase rule"<a class="footnote-ref" id="fnref:28" href="#fn:28"><sup>28</sup></a></li> <li><a href="/facts/Default_effect/p0MRcrWu">Default effect</a> – Tendency to accept the default option<a class="footnote-ref" id="fnref:29" href="#fn:29"><sup>29</sup></a></li> <li><a href="/facts/Priority_heuristic/pYvhl448">Priority heuristic</a><a class="footnote-ref" id="fnref:30" href="#fn:30"><sup>30</sup></a></li> <li><i>Take-the-first heuristic</i><a class="footnote-ref" id="fnref:31" href="#fn:31"><sup>31</sup></a></li></ul> <h3>Recognition-based decisions</h3> <p>A class whose function is to determine and filter out superfluous things.<a class="footnote-ref" id="fnref:32" href="#fn:32"><sup>32</sup></a> </p> <ul><li><a href="/facts/Recognition_heuristic/dlpPn6eu">Recognition heuristic</a> – Decision-making Concept in PsychologyPages displaying wikidata descriptions as a fallback<a class="footnote-ref" id="fnref:33" href="#fn:33"><sup>33</sup></a><a class="footnote-ref" id="fnref:34" href="#fn:34"><sup>34</sup></a></li> <li><a href="/facts/Fluency_heuristic/Yy3XHQon">Fluency heuristic</a> – Mental heuristic<a class="footnote-ref" id="fnref:35" href="#fn:35"><sup>35</sup></a><a class="footnote-ref" id="fnref:36" href="#fn:36"><sup>36</sup></a></li></ul> <h3>Tracking heuristics</h3> <p class="note">For broader coverage of this topic, see <a href="/facts/Predation/JEGdrX3f">Predation</a>.</p> <p><i>Tracking heuristics</i> is a <a href="/facts/Class_(knowledge_representation)/e3BfgFty">class</a> of heuristics.<a class="footnote-ref" id="fnref:37" href="#fn:37"><sup>37</sup></a> </p> <ul><li><a href="/facts/Gaze_heuristic/M7PKy4kV">Gaze heuristic</a><a class="footnote-ref" id="fnref:38" href="#fn:38"><sup>38</sup></a></li> <li><a href="/facts/Pointing_and_calling/ESHvbAsF">Pointing and calling</a> – Railway safety technique</li></ul> <h3>Trade-off</h3> <ul><li><a href="/facts/Trade-off/xY3nXnJN">Trade-off</a> – Situational decision<a class="footnote-ref" id="fnref:39" href="#fn:39"><sup>39</sup></a> <ul><li><i>Tallying heuristic</i><a class="footnote-ref" id="fnref:40" href="#fn:40"><sup>40</sup></a><a class="footnote-ref" id="fnref:41" href="#fn:41"><sup>41</sup></a></li> <li><i>Equality heuristic</i><a class="footnote-ref" id="fnref:42" href="#fn:42"><sup>42</sup></a></li></ul></li></ul> <h3>Social heuristics</h3> <p><a href="/facts/Social_heuristics/xm26NhCo">Social heuristics</a> – Decision-making processes in social environments<a class="footnote-ref" id="fnref:43" href="#fn:43"><sup>43</sup></a> </p> <ul><li><a href="/facts/Imitation/rh2QNCGr">Imitation</a> – Behaviour in which an individual observes and replicates another's behaviour<a class="footnote-ref" id="fnref:44" href="#fn:44"><sup>44</sup></a></li> <li><a href="/facts/Tit_for_tat/YQ2HshrX">Tit for tat</a> – English saying meaning "equivalent retaliation"<a class="footnote-ref" id="fnref:45" href="#fn:45"><sup>45</sup></a></li> <li><a href="/facts/Wisdom_of_the_crowd/qC8v2k9T">Wisdom of the crowd</a> – Collective perception of a group of people<a class="footnote-ref" id="fnref:46" href="#fn:46"><sup>46</sup></a></li></ul> <h3>Epistemic heuristics</h3> <p class="note">For broader coverage of this topic, see <a href="/facts/Tacit_assumption/rMjEC2GW">Tacit assumption</a>.</p> <ul><li><a href="/facts/Propositional_attitude/KpEYEPGc">Propositional attitude</a> – Concept in epistemology<a class="footnote-ref" id="fnref:47" href="#fn:47"><sup>47</sup></a></li> <li><a href="/facts/Essence/X1uqmlSm">Essence</a> – That which makes or defines an entity what it is<a class="footnote-ref" id="fnref:48" href="#fn:48"><sup>48</sup></a></li> <li><a href="/facts/Analysis/heFlRrYd">Analysis</a> – Process of understanding a complex topic or substance<a class="footnote-ref" id="fnref:49" href="#fn:49"><sup>49</sup></a></li> <li><a href="/facts/Falsifiability/IlxN7cYB">Falsifiability</a> – Property of a statement that can be logically contradicted<a class="footnote-ref" id="fnref:50" href="#fn:50"><sup>50</sup></a></li> <li><a href="/facts/Hierarchy_of_evidence/1BaXTJHx">Hierarchy of evidence</a> – Heuristic ranking science research results</li></ul> <h3>Behavioral economics</h3> <p class="note">Main article: <a href="/facts/Behavioral_economics/bbSgL7Ad">Behavioral economics</a></p> <ul><li><a href="/facts/Affect_heuristic/G0U0C8MP">Affect heuristic</a> – Mental shortcut based on emotion<a class="footnote-ref" id="fnref:51" href="#fn:51"><sup>51</sup></a></li> <li><a href="/facts/Feedback/XnuWH3zp">Feedback</a> – Process where information about current status is used to influence future status<a class="footnote-ref" id="fnref:52" href="#fn:52"><sup>52</sup></a></li> <li><a href="/facts/Reinforcement/bn271VLa">Reinforcement</a> – Consequence affecting an organism's future behavior<a class="footnote-ref" id="fnref:53" href="#fn:53"><sup>53</sup></a></li> <li><a href="/facts/Stimulus%25E2%2580%2593response_model/uH9bA3zR">Stimulus–response model</a> – Conceptual framework in psychology<a class="footnote-ref" id="fnref:54" href="#fn:54"><sup>54</sup></a></li></ul> <h3>Others</h3> <ul><li><a href="/facts/Satisficing/Bzcsq4l8">Satisficing</a> – Cognitive heuristic of searching for an acceptable decision<a class="footnote-ref" id="fnref:55" href="#fn:55"><sup>55</sup></a><a class="footnote-ref" id="fnref:56" href="#fn:56"><sup>56</sup></a><a class="footnote-ref" id="fnref:57" href="#fn:57"><sup>57</sup></a></li> <li><a href="/facts/Representativeness_heuristic/GDKvCmLR">Representativeness heuristic</a> – Tool for assisting judgement in uncertainty<a class="footnote-ref" id="fnref:58" href="#fn:58"><sup>58</sup></a><a class="footnote-ref" id="fnref:59" href="#fn:59"><sup>59</sup></a><a class="footnote-ref" id="fnref:60" href="#fn:60"><sup>60</sup></a></li> <li><a href="/facts/Availability_heuristic/BMwkXqxD">Availability heuristic</a> – Bias towards recently acquired information<a class="footnote-ref" id="fnref:61" href="#fn:61"><sup>61</sup></a><a class="footnote-ref" id="fnref:62" href="#fn:62"><sup>62</sup></a></li> <li><a href="/facts/Awareness/PazhgWFC">Awareness</a> – Perception or knowledge of something<a class="footnote-ref" id="fnref:63" href="#fn:63"><sup>63</sup></a></li> <li><a href="/facts/Base_and_superstructure/v2USoBjM">Base and superstructure</a> – Model of society in Marxist theory<a class="footnote-ref" id="fnref:64" href="#fn:64"><sup>64</sup></a></li> <li><a href="/facts/Social_organism/7D0bl22I">Social organism</a> – Model of social interactions<a class="footnote-ref" id="fnref:65" href="#fn:65"><sup>65</sup></a></li> <li><a href="/facts/Dialectic/NTL69ms6">Dialectic</a> – Method of reasoning via argumentation and contradiction<a class="footnote-ref" id="fnref:66" href="#fn:66"><sup>66</sup></a></li> <li><a href="/facts/Continuum_limit/bSDQhaU9">Continuum limit</a> – Continuum limit in lattice models<a class="footnote-ref" id="fnref:67" href="#fn:67"><sup>67</sup></a></li> <li><a href="/facts/Johari_window/5pdPaph2">Johari window</a> – Technique in personality development</li> <li><a href="/facts/Social_rationality/qKCQPxqh">Social rationality</a></li> <li><a href="/facts/Desert_(philosophy)/teXCbGIN">Desert (philosophy)</a> – Condition of being deserving of something, whether good or bad<a class="footnote-ref" id="fnref:68" href="#fn:68"><sup>68</sup></a></li> <li><a href="/facts/Less-is-better_effect/XM8JGUQ9">Less-is-better effect</a> – Cognitive bias</li> <li><a href="/facts/Simple_random_sample/vDnCHnNx">Minimalist heuristic</a><a class="footnote-ref" id="fnref:69" href="#fn:69"><sup>69</sup></a></li> <li><a href="/facts/Unification_of_theories_in_physics/GV8h3FNJ">Unification of theories in physics</a> – Idea of connecting all of physics into one set of equations<a class="footnote-ref" id="fnref:70" href="#fn:70"><sup>70</sup></a></li> <li><a href="/facts/Backward_induction/x4DwF49D">Backward induction</a> – Process of reasoning backwards in sequence</li></ul> <h3>Meta-heuristic</h3> <p class="note">Main article: <a href="/facts/Metaheuristic/ECF71Rez">Metaheuristic</a></p> <p class="note">For broader coverage of this topic, see <a href="/facts/List_of_metaphor-based_metaheuristics/6zoAh906">List of metaphor-based metaheuristics</a>.</p> <ul><li><i>Optimality</i><a class="footnote-ref" id="fnref:71" href="#fn:71"><sup>71</sup></a> <ul><li><a href="/facts/Survival_of_the_fittest/0HmV10AD">Survival of the fittest</a> – Phrase to describe the mechanism of natural selection<a class="footnote-ref" id="fnref:72" href="#fn:72"><sup>72</sup></a></li> <li><a href="/facts/Mechanical_equilibrium/N2XKaK6s">Mechanical equilibrium</a> – When the net force on a particle is zero<a class="footnote-ref" id="fnref:73" href="#fn:73"><sup>73</sup></a></li> <li><a href="/facts/Chemical_equilibrium/y8ToYXHp">Chemical equilibrium</a> – When the ratio of reactants to products of a chemical reaction is constant with time<a class="footnote-ref" id="fnref:74" href="#fn:74"><sup>74</sup></a></li> <li><a href="/facts/Homeostasis/rloTGAHa">Homeostasis</a> – State of steady internal conditions maintained by living things<a class="footnote-ref" id="fnref:75" href="#fn:75"><sup>75</sup></a></li> <li><a href="/facts/Entropy/s52IXeFz">Entropy</a> – Property of a thermodynamic system<a class="footnote-ref" id="fnref:76" href="#fn:76"><sup>76</sup></a></li></ul></li></ul> <h2 id="history">History</h2> <p class="note">Main article: <a href="/facts/Analysis/heFlRrYd">Analysis</a></p> <p class="note">For broader coverage of this topic, see <a href="/facts/Dialectic/NTL69ms6">Dialectic</a>.</p> <p><a href="/facts/George_Polya/cYgGdvJu">George Polya</a> studied and published on heuristics in 1945.<a class="footnote-ref" id="fnref:77" href="#fn:77"><sup>77</sup></a> Polya (1945) cites <a href="/facts/Pappus_of_Alexandria/UABLMu0m">Pappus of Alexandria</a> as having written a <a href="/facts/Text_(literary_theory)/GwYDS61T">text</a> that Polya dubs <i>Heuristic</i>.<a class="footnote-ref" id="fnref:78" href="#fn:78"><sup>78</sup></a> Pappus' heuristic problem-solving methods consist of <a href="/facts/Analysis/heFlRrYd">analysis</a> and synthesis.<a class="footnote-ref" id="fnref:79" href="#fn:79"><sup>79</sup></a> </p> <h3>Notable</h3> <h4>Figures</h4> <ul><li><a href="/facts/George_Polya/cYgGdvJu">George Polya</a><a class="footnote-ref" id="fnref:80" href="#fn:80"><sup>80</sup></a><a class="footnote-ref" id="fnref:81" href="#fn:81"><sup>81</sup></a><a class="footnote-ref" id="fnref:82" href="#fn:82"><sup>82</sup></a></li> <li><a href="/facts/Herbert_A._Simon/NNHhQ2bS">Herbert A. Simon</a><a class="footnote-ref" id="fnref:83" href="#fn:83"><sup>83</sup></a></li> <li><a href="/facts/Daniel_Kahneman/YnI99yAy">Daniel Kahneman</a><a class="footnote-ref" id="fnref:84" href="#fn:84"><sup>84</sup></a></li> <li><a href="/facts/Amos_Tversky/DfvE3URJ">Amos Tversky</a><a class="footnote-ref" id="fnref:85" href="#fn:85"><sup>85</sup></a><a class="footnote-ref" id="fnref:86" href="#fn:86"><sup>86</sup></a></li> <li><a href="/facts/Gerd_Gigerenzer/tU4H5r88">Gerd Gigerenzer</a><a class="footnote-ref" id="fnref:87" href="#fn:87"><sup>87</sup></a></li> <li><a href="/facts/Judea_Pearl/J2JUSAfe">Judea Pearl</a><a class="footnote-ref" id="fnref:88" href="#fn:88"><sup>88</sup></a></li> <li><a href="/facts/Robin_Dunbar/DBN79S6y">Robin Dunbar</a><a class="footnote-ref" id="fnref:89" href="#fn:89"><sup>89</sup></a></li> <li><a href="/facts/David_Perkins_Page/mWGX8uCL">David Perkins Page</a><a class="footnote-ref" id="fnref:90" href="#fn:90"><sup>90</sup></a></li> <li><a href="/facts/Herbert_Spencer/d1qsSq4r">Herbert Spencer</a><a class="footnote-ref" id="fnref:91" href="#fn:91"><sup>91</sup></a></li> <li><a href="/facts/Charles_Alexander_McMurry/hbHQaLgx">Charles Alexander McMurry</a><a class="footnote-ref" id="fnref:92" href="#fn:92"><sup>92</sup></a></li> <li><a href="/facts/Frank_Morton_McMurry/3vsqqqfL">Frank Morton McMurry</a><a class="footnote-ref" id="fnref:93" href="#fn:93"><sup>93</sup></a></li> <li><a href="/facts/Lawrence_Zalcman/zsbEu6Sd">Lawrence Zalcman</a><a class="footnote-ref" id="fnref:94" href="#fn:94"><sup>94</sup></a></li> <li><a href="/facts/Imre_Lakatos/yA7PxtFW">Imre Lakatos</a><a class="footnote-ref" id="fnref:95" href="#fn:95"><sup>95</sup></a></li> <li><a href="/facts/William_C._Wimsatt/U2lGRVYb">William C. Wimsatt</a><a class="footnote-ref" id="fnref:96" href="#fn:96"><sup>96</sup></a><a class="footnote-ref" id="fnref:97" href="#fn:97"><sup>97</sup></a></li> <li><a href="/facts/Alan_Hodgkin/YUY3C2Hd">Alan Hodgkin</a><a class="footnote-ref" id="fnref:98" href="#fn:98"><sup>98</sup></a></li> <li><a href="/facts/Andrew_Huxley/DzFkDD4W">Andrew Huxley</a><a class="footnote-ref" id="fnref:99" href="#fn:99"><sup>99</sup></a></li></ul> <h4>Works</h4> <ul><li><i><a href="/facts/Meno/YYGdjFSA">Meno</a></i><a class="footnote-ref" id="fnref:100" href="#fn:100"><sup>100</sup></a></li> <li><i><a href="/facts/How_to_solve_it/4bpj2cxp">How to solve it</a></i><a class="footnote-ref" id="fnref:101" href="#fn:101"><sup>101</sup></a></li> <li><i><a href="/facts/Mathematics_and_Plausible_Reasoning/bpIcmhGY">Mathematics and Plausible Reasoning</a></i><a class="footnote-ref" id="fnref:102" href="#fn:102"><sup>102</sup></a></li></ul> <h3>Contemporary</h3> <p>The study of heuristics in human <a href="/facts/Decision-making/NbJYprSw">decision-making</a> was developed in the 1970s and the 1980s, by the psychologists <a href="/facts/Amos_Tversky/DfvE3URJ">Amos Tversky</a> and <a href="/facts/Daniel_Kahneman/YnI99yAy">Daniel Kahneman</a>,<a class="footnote-ref" id="fnref:103" href="#fn:103"><sup>103</sup></a> although the concept had been originally introduced by the <a href="/facts/List_of_Nobel_laureates/3vGWzdwa">Nobel laureate</a> <a href="/facts/Herbert_A._Simon/NNHhQ2bS">Herbert A. Simon</a>. Simon's original primary object of research was problem solving that showed that we operate within what he calls <i><a href="/facts/Bounded_rationality/43sxWwHU">bounded rationality</a></i>. He coined the term <i><a href="/facts/Satisficing/Bzcsq4l8">satisficing</a></i>, which denotes a situation in which people seek solutions, or accept choices or judgements, that are "good enough" for their purposes although they could be optimised.<a class="footnote-ref" id="fnref:104" href="#fn:104"><sup>104</sup></a> </p><p><a href="/facts/Rudolf_Groner/pKfII6df">Rudolf Groner</a> analysed the history of heuristics from its roots in ancient Greece up to contemporary work in <a href="/facts/Cognitive_psychology/GnzIgx2I">cognitive psychology</a> and <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a>,<a class="footnote-ref" id="fnref:105" href="#fn:105"><sup>105</sup></a> proposing a cognitive style "heuristic versus algorithmic thinking", which can be assessed by means of a validated <a href="/facts/Questionnaire/15o4lgqm">questionnaire</a>.<a class="footnote-ref" id="fnref:106" href="#fn:106"><sup>106</sup></a> </p> <h3>Adaptive toolbox</h3> <p>The <i>adaptive toolbox</i> contains strategies for fabricating heuristic devices.<a class="footnote-ref" id="fnref:107" href="#fn:107"><sup>107</sup></a> The core mental capacities are <a href="/facts/Recall_(memory)/flxSh2qP">recall (memory)</a>, <a href="/facts/Frequency/QUSbPaW8">frequency</a>, <a href="/facts/Object_permanence/lB8jA0Jh">object permanence</a>, and <a href="/facts/Imitation/rh2QNCGr">imitation</a>.<a class="footnote-ref" id="fnref:108" href="#fn:108"><sup>108</sup></a> <a href="/facts/Gerd_Gigerenzer/tU4H5r88">Gerd Gigerenzer</a> and his research group argued that models of heuristics need to be formal to allow for predictions of behavior that can be tested.<a class="footnote-ref" id="fnref:109" href="#fn:109"><sup>109</sup></a> They study the fast and frugal heuristics in the "adaptive toolbox" of individuals or institutions, and the <a href="/facts/Ecological_rationality/C5qFkD89">ecological rationality</a> of these heuristics; that is, the conditions under which a given heuristic is likely to be successful.<a class="footnote-ref" id="fnref:110" href="#fn:110"><sup>110</sup></a> The descriptive study of the "adaptive toolbox" is done by observation and experiment, while the prescriptive study of <a href="/facts/Ecological_rationality/C5qFkD89">ecological rationality</a> requires mathematical analysis and computer simulation. Heuristics – such as the <a href="/facts/Recognition_heuristic/dlpPn6eu">recognition heuristic</a>, the <a href="/facts/Take-the-best_heuristic/IcppA5Ih">take-the-best heuristic</a> and <a href="/facts/Fast-and-frugal_trees/gCjjbHWe">fast-and-frugal trees</a> – have been shown to be effective in predictions, particularly in situations of uncertainty. It is often said that heuristics trade accuracy for effort but this is only the case in situations of risk. Risk refers to situations where all possible actions, their outcomes and probabilities are known. In the absence of this information, that is under uncertainty, heuristics can achieve higher accuracy with lower effort.<a class="footnote-ref" id="fnref:111" href="#fn:111"><sup>111</sup></a> This finding, known as a <a href="/facts/Less-is-better_effect/XM8JGUQ9">less-is-more effect</a>, would not have been found without formal models. The valuable insight of this program is that heuristics are effective not despite their simplicity – but because of it. Furthermore, <a href="/facts/Gerd_Gigerenzer/tU4H5r88">Gigerenzer</a> and Wolfgang Gaissmaier found that both individuals and organisations rely on heuristics in an adaptive way.<a class="footnote-ref" id="fnref:112" href="#fn:112"><sup>112</sup></a> </p> <h3>Cognitive-experiential self-theory</h3> <p>Heuristics, through greater refinement and research, have begun to be applied to other theories, or be explained by them. For example, the <a href="/facts/Cognitive-experiential_self-theory/7RuDsXyu">cognitive-experiential self-theory</a> (CEST) is also an adaptive view of heuristic processing. CEST breaks down two systems that process information. At some times, roughly speaking, individuals consider issues rationally, systematically, logically, deliberately, effortfully, and verbally. On other occasions, individuals consider issues intuitively, effortlessly, globally, and emotionally.<a class="footnote-ref" id="fnref:113" href="#fn:113"><sup>113</sup></a> From this perspective, heuristics are part of a larger experiential processing system that is often adaptive, but vulnerable to error in situations that require logical analysis.<a class="footnote-ref" id="fnref:114" href="#fn:114"><sup>114</sup></a> </p> <h3>Attribute substitution</h3> <p>In 2002, <a href="/facts/Daniel_Kahneman/YnI99yAy">Daniel Kahneman</a> and <a href="/facts/Shane_Frederick/HQ4TxYGp">Shane Frederick</a> proposed that cognitive heuristics work by a process called <i><a href="/facts/Attribute_substitution/bvSFqCEY">attribute substitution</a></i>, which happens without conscious awareness.<a class="footnote-ref" id="fnref:115" href="#fn:115"><sup>115</sup></a> According to this theory, when somebody makes a judgement (of a "target attribute") that is computationally complex, a more easily calculated "heuristic attribute" is substituted. In effect, a cognitively difficult problem is dealt with by answering a rather simpler problem, without being aware of this happening.<a class="footnote-ref" id="fnref:116" href="#fn:116"><sup>116</sup></a> This theory explains cases where judgements fail to show <a href="/facts/Regression_toward_the_mean/l7hDWAVR">regression toward the mean</a>.<a class="footnote-ref" id="fnref:117" href="#fn:117"><sup>117</sup></a> Heuristics can be considered to reduce the complexity of clinical judgments in health care.<a class="footnote-ref" id="fnref:118" href="#fn:118"><sup>118</sup></a> </p> <h2 id="academic-disciplines">Academic disciplines</h2> <h3>Psychology</h3> <p class="note">Main article: <a href="/facts/Heuristic_(psychology)/gwyv5AkX">Heuristic (psychology)</a></p> <p class="note">For broader coverage of this topic, see <a href="/facts/Phenomenology_(psychology)/Rse4u0LN">Phenomenology (psychology)</a>.</p> <p class="note">See also: <a href="/facts/Decision-making/NbJYprSw">Decision-making</a></p> <p>In <a href="/facts/Psychology/duoqW7Sz">psychology</a>, heuristics are simple, efficient rules, either learned or inculcated by evolutionary processes. These psychological heuristics have been proposed to explain how people make decisions, come to judgements, and solve problems. These rules typically come into play when people face complex problems or incomplete information. Researchers employ various methods to test whether people use these rules. The rules have been shown to work well under most circumstances, but in certain cases can lead to systematic errors or <a href="/facts/Cognitive_bias/49YKVhmN">cognitive biases</a>.<a class="footnote-ref" id="fnref:119" href="#fn:119"><sup>119</sup></a> </p> <h3>Philosophy</h3> <p>A heuristic device is used when an entity <i>X</i> exists to enable understanding of, or knowledge concerning, some other entity <i>Y</i>. </p><p>A good example is a <a href="/facts/Physical_model/remaCtIh">model</a> that, as <a href="/facts/Map%2fterritory_relation/Mf2HAkTw">it is never identical with what it models</a>, is a heuristic device to enable understanding of what it models. Stories, metaphors, etc., can also be termed heuristic in this sense. A classic example is the notion of <a href="/facts/Utopia/XWyF0zk4">utopia</a> as described in <a href="/facts/Plato/sPedMo74">Plato</a>'s best-known work, <i><a href="/facts/Plato%2527s_Republic/GKJ1YuQe">The Republic</a></i>. This means that the "ideal city" as depicted in <i>The Republic</i> is not given as something to be pursued, or to present an orientation-point for development. Rather, it shows how things would have to be connected, and how one thing would lead to another (often with highly problematic results), if one opted for certain principles and carried them through rigorously. </p><p><i>Heuristic</i> is also often used as a <a href="/facts/Noun/SsnDNfkX">noun</a> to describe a <a href="/facts/Rule_of_thumb/RopfGsJ0">rule of thumb</a>, procedure, or method.<a class="footnote-ref" id="fnref:120" href="#fn:120"><sup>120</sup></a> Philosophers of science have emphasised the importance of heuristics in creative thought and the construction of scientific theories.<a class="footnote-ref" id="fnref:121" href="#fn:121"><sup>121</sup></a> Seminal works include <a href="/facts/Karl_Popper/RpLqMrz5">Karl Popper</a>'s <i><a href="/facts/The_Logic_of_Scientific_Discovery/H1GX1YL0">The Logic of Scientific Discovery</a></i> and others by <a href="/facts/Imre_Lakatos/yA7PxtFW">Imre Lakatos</a>,<a class="footnote-ref" id="fnref:122" href="#fn:122"><sup>122</sup></a> <a href="/facts/Lindley_Darden/CvgJFMn5">Lindley Darden</a>, and <a href="/facts/William_C._Wimsatt/U2lGRVYb">William C. Wimsatt</a>. </p> <h3>Law</h3> <p>In <a href="/facts/Legal_theory/wNTsXP4F">legal theory</a>, especially in the theory of <a href="/facts/Law_and_economics/za7teyY2">law and economics</a>, heuristics are used in the <a href="/facts/Law/luzxWkg0">law</a> when <a href="/facts/Case_method/FENt9Y3L">case-by-case analysis</a> would be impractical, insofar as "practicality" is defined by the interests of a governing body.<a class="footnote-ref" id="fnref:123" href="#fn:123"><sup>123</sup></a> </p><p>The present securities regulation regime largely assumes that all investors act as perfectly rational persons. In truth, actual investors face cognitive limitations from biases, heuristics, and framing effects. For instance, in all states in the United States the <a href="/facts/Legal_drinking_age/ABAsHarg">legal drinking age</a> for unsupervised persons is 21 years, because it is argued that people need to be mature enough to make decisions involving the risks of <a href="/facts/Alcohol_(drug)/IeK2fVd5">alcohol</a> consumption. However, assuming people mature at different rates, the specific age of 21 would be too late for some and too early for others. In this case, the somewhat arbitrary delineation is used because it is impossible or impractical to tell whether an individual is sufficiently mature for society to trust them with that kind of responsibility. Some proposed changes, however, have included the completion of an alcohol education course rather than the attainment of 21 years of age as the criterion for legal alcohol possession. This would put youth alcohol policy more on a case-by-case basis and less on a heuristic one, since the completion of such a course would presumably be voluntary and not uniform across the population. </p><p>The same reasoning applies to <a href="/facts/Patent_law/LjT2bG3A">patent law</a>. <a href="/facts/Patent/LjT2bG3A">Patents</a> are justified on the grounds that inventors must be protected so they have incentive to invent. It is therefore argued that it is in society's best interest that inventors receive a temporary government-granted <a href="/facts/Monopoly/BXIQWUmR">monopoly</a> on their idea, so that they can recoup investment costs and make economic profit for a limited period. In the United States, the length of this temporary monopoly is 20 years from the date the patent application was filed, though the monopoly does not actually begin until the application has matured into a patent. However, like the drinking age problem above, the specific length of time would need to be different for every product to be efficient. A 20-year term is used because it is difficult to tell what the number should be for any individual patent. More recently, some, including <a href="/facts/University_of_North_Dakota/ysr2yeGS">University of North Dakota</a> law professor Eric E. Johnson, have argued that patents in different kinds of industries – such as <a href="/facts/Software_patent/wI6RrYh3">software patents</a> – should be protected for different lengths of time.<a class="footnote-ref" id="fnref:124" href="#fn:124"><sup>124</sup></a> </p> <h3>Artificial intelligence</h3> <p>The <a href="/facts/Bias%25E2%2580%2593variance_tradeoff/xqDnRpeQ">bias–variance tradeoff</a> gives insight into describing the less-is-more strategy.<a class="footnote-ref" id="fnref:125" href="#fn:125"><sup>125</sup></a> A <a href="/facts/Heuristic_(computer_science)/PM2qkoa2">heuristic</a> can be used in <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> systems while searching a <a href="/facts/Solution_space/cJUKNCmM">solution space</a>. The heuristic is derived by using some function that is put into the system by the designer, or by adjusting the weight of branches based on how likely each branch is to lead to a <a href="/facts/Goal_node_(computer_science)/D0wWJJ6g">goal node</a>. </p> <h3>Behavioural economics</h3> <p>Heuristics refers to the cognitive shortcuts that individuals use to simplify decision-making processes in economic situations. <a href="/facts/Behavioral_economics/bbSgL7Ad">Behavioral economics</a> is a field that integrates insights from psychology and economics to better understand how people make decisions. </p><p>Anchoring and adjustment is one of the most extensively researched heuristics in behavioural economics. Anchoring is the tendency of people to make future judgements or conclusions based too heavily on the original information supplied to them. This initial knowledge functions as an anchor, and it can influence future judgements even if the anchor is entirely unrelated to the decisions at hand. Adjustment, on the other hand, is the process through which individuals make gradual changes to their initial judgements or conclusions. </p><p><a href="/facts/Anchoring_and_adjustment/V793WguQ">Anchoring and adjustment</a> has been observed in a wide range of decision-making contexts, including financial decision-making, consumer behavior, and negotiation. Researchers have identified a number of strategies that can be used to mitigate the effects of anchoring and adjustment, including providing multiple anchors, encouraging individuals to generate alternative anchors, and providing cognitive prompts to encourage more deliberative decision-making. </p><p>Other heuristics studied in behavioral economics include the <a href="/facts/Representativeness_heuristic/GDKvCmLR">representativeness heuristic</a>, which refers to the tendency of individuals to categorize objects or events based on how similar they are to typical examples,<a class="footnote-ref" id="fnref:126" href="#fn:126"><sup>126</sup></a> and the <a href="/facts/Availability_heuristic/BMwkXqxD">availability heuristic</a>, which refers to the tendency of individuals to judge the likelihood of an event based on how easily it comes to mind.<a class="footnote-ref" id="fnref:127" href="#fn:127"><sup>127</sup></a> </p> <h2 id="stereotyping">Stereotyping</h2> <p><a href="/facts/Stereotype/UDveLU8n">Stereotyping</a> is a type of heuristic that people use to form opinions or make judgements about things they have never seen or experienced.<a class="footnote-ref" id="fnref:128" href="#fn:128"><sup>128</sup></a> They work as a mental shortcut to assess everything from the social status of a person (based on their actions),<a class="footnote-ref" id="fnref:129" href="#fn:129"><sup>129</sup></a> to classifying a plant as a tree based on it being tall, having a trunk, and that it has leaves (even though the person making the evaluation might never have seen that particular type of tree before). </p><p>Stereotypes, as first described by journalist <a href="/facts/Walter_Lippmann/kl0UD2N9">Walter Lippmann</a> in his book <i><a href="/facts/Public_Opinion_(book)/613TfwMk">Public Opinion</a></i> (1922), are the pictures we have in our heads that are built around experiences as well as what we are told about the world.<a class="footnote-ref" id="fnref:130" href="#fn:130"><sup>130</sup></a><a class="footnote-ref" id="fnref:131" href="#fn:131"><sup>131</sup></a> </p> <h2 id="see-also">See also</h2> Look up <i>heuristic</i> in Wiktionary, the free dictionary. Wikibooks has more on the topic of: <i>Heuristic</i> <ul><li><a href="/facts/ACT-R/QA8cYaAr">ACT-R</a> – SoftwarePages displaying short descriptions with no spaces</li> <li><a href="/facts/Algorithm/fnl5NmRt">Algorithm</a> – Sequence of operations for a task</li> <li><a href="/facts/Applied_epistemology/lIletask">Applied epistemology</a> – Application of epistemology in specific fields</li> <li><a href="/facts/Branch_and_bound/mhALtav7">Branch and bound</a> – Optimization by eliminating non optimal solutions to sub-problems</li> <li><a href="/facts/Coherence_(philosophical_gambling_strategy)/mrqpcJlP">Coherence (philosophical gambling strategy)</a> – Thought experiment, to justify Bayesian probabilityPages displaying short descriptions of redirect targets</li> <li><a href="/facts/Decision_theory/TbXcQX0v">Decision theory</a> – Branch of applied probability theory</li> <li><a href="/facts/Embodied_cognition/zgewISTW">Embodied cognition</a> – Interdisciplinary theory</li> <li><a href="/facts/Failure_mode_and_effects_analysis/fdjxbXxP">Failure mode and effects analysis</a> – Analysis of potential system failures</li> <li><a href="/facts/Game_theory/zkEIj1ya">Game theory</a> – Mathematical models of strategic interactions</li> <li><a href="/facts/Heuristic-systematic_model_of_information_processing/KtXuVKXf">Heuristic-systematic model of information processing</a> – A dual process theory of persuasion</li> <li><a href="/facts/Heuristics_in_judgment_and_decision-making/gwyv5AkX">Heuristics in judgment and decision-making</a> – Simple strategies or mental processes involved in making quick decisionsPages displaying short descriptions of redirect targets</li> <li><a href="/facts/Ideal_type/eS9enYsC">Ideal type</a> – Typological term</li> <li><a href="/facts/List_of_biases_in_judgment_and_decision_making/1d1rwe2N">List of biases in judgment and decision making</a></li> <li><a href="/facts/Metalepsis/MNBRJuyF">Metalepsis</a> – Figure of speech<a class="footnote-ref" id="fnref:132" href="#fn:132"><sup>132</sup></a></li> <li><a href="/facts/Methodic_school/062e6Qw6">Methodic school</a> – School of medicine in ancient Greece and Rome<a class="footnote-ref" id="fnref:133" href="#fn:133"><sup>133</sup></a></li> <li><a href="/facts/Necessity_and_sufficiency/G45aDCY9">Necessity and sufficiency</a> – Terms to describe a conditional relationship between two statements</li> <li><a href="/facts/Neuroheuristics/Vng5YADY">Neuroheuristics</a></li> <li><a href="/facts/Nudge_theory/ontHhsVr">Nudge theory</a> – Concept in behavioral economics, political theory and behavioral sciences</li> <li><a href="/facts/Predictive_coding/DRRQQcTB">Predictive coding</a> – Theory of brain function</li> <li><a href="/facts/Principle_of_good_enough/5NQjMGAp">Principle of good enough</a> – Principle of social research</li> <li><a href="/facts/Priority_heuristic/pYvhl448">Priority heuristic</a></li> <li><a href="/facts/Prospect_theory/LUYof79K">Prospect theory</a> – Theory of behavioral economics</li> <li><a href="/facts/Rule-based_system/xmlMNr7j">Rule-based system</a> – Type of computer system</li> <li><a href="/facts/Rule_of_inference/QV5NklQ9">Rule of inference</a> – Method of deriving conclusions<a class="footnote-ref" id="fnref:134" href="#fn:134"><sup>134</sup></a></li> <li><a href="/facts/SCAMPER/qPvdpSPu">SCAMPER</a> – SCAMPER is an acronym for the creative development process proposed by Alex Faickney Osborn.</li> <li><a href="/facts/Situated_cognition/ug0JlI77">Situated cognition</a> – Hypothesis that knowing is inseparable from doing</li> <li><a href="/facts/Six_Thinking_Hats/yMhp12T2">Six Thinking Hats</a> – 1985 book by Maltese Dr. Edward de Bono</li> <li><a href="/facts/Social_heuristics/xm26NhCo">Social heuristics</a> – Decision-making processes in social environments</li> <li><a href="/facts/Subjective_expected_utility/BtPC9adh">Subjective expected utility</a> – Concept in decision theory</li> <li><a href="/facts/Thought_experiment/GXlr0lz9">Thought experiment</a> – Hypothetical situation</li> <li><a href="/facts/TRIZ/6Lw9YjMK">TRIZ</a> – Problem-solving tools</li> <li><a href="/facts/Tutorial/PrsIwPo4">Tutorial</a> – Type of educational intervention</li></ul> <h2 id="further-reading">Further reading</h2> <ul><li><i>How To Solve It: Modern Heuristics</i>, Zbigniew Michalewicz and David B. Fogel, Springer Verlag, 2000. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 3-540-66061-5</li> <li><a href="/facts/Stuart_J._Russell/f6EbRvSV">Russell, Stuart J.</a>; <a href="/facts/Peter_Norvig/GkCUHH4H">Norvig, Peter</a> (2021). <i><a href="/facts/Artificial_Intelligence%3a_A_Modern_Approach/hPd3v5kh">Artificial Intelligence: A Modern Approach</a></i> (4th ed.). Hoboken: Pearson. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 9780134610993. <a href="/facts/LCCN_(identifier)/5hb5Cw2V">LCCN</a> <a href="https://lccn.loc.gov/20190474">20190474</a>.</li> <li><a href="http://www-stat.stanford.edu/~cgates/PERSI/papers/thinking.pdf">The Problem of Thinking Too Much</a> <a href="https://web.archive.org/web/20131019160106/http://www-stat.stanford.edu/~cgates/PERSI/papers/thinking.pdf">Archived</a> 2013-10-19 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a>, 11 December 2002, <a href="/facts/Persi_Diaconis/AsI5mBLp">Persi Diaconis</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>(/hjʊˈrɪstɪk/; from Ancient Greek εὑρίσκω (heurískō) 'method of discovery' Romanycia, Marc; Pelletier, Francis; Pelletier, Jeffry (1985). "What is a heuristic?". Computational Intelligence. 1 (1): 47–58. doi:10.1111/j.1467-8640.1985.tb00058.x. Retrieved 11 May 2024. heuriskein (ancient Greek) and heurisricus (Latin): 'to find out, discover.') <a href="/wiki/Help:IPA/English" target="_blank">/wiki/Help:IPA/English</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Groner, Rudolf; Groner, Marina; Bischof, Walter (2014). Methods of heuristics. Routledge. 'guiding discovery' or 'improving problem solving' ... its origin in ancient Greece where the verb 'heuriskein' means to find. <a href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=76a213b007a1c8072760b59b58df40729fa1f3c1" target="_blank">https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=76a213b007a1c8072760b59b58df40729fa1f3c1</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. The word heuristic is taken directly from the Greek verb, heuriskein, 'to discover'. As a noun it is defined as 'a technique of discovery' and as an adjective, it means 'serving to guide, discover, or reveal'. The more common designation for all of this is 'the discovery method'. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Hertwig, Ralph; Pachur, Thorsten (2015). "Heuristics, history of". International Encyclopedia of the Social & Behavioral Sciences: 829–835. doi:10.1016/B978-0-08-097086-8.03221-9. ISBN 978-0-08-097087-5. Retrieved 10 May 2024. The origin of the term goes back to the Ancient Greek verb heuriskein, which means 'to find out' or 'to discover.' Heuristics are sometimes also referred to as 'mental shortcuts' or 'rules of thumb.' <a href="978-0-08-097087-5" target="_blank">978-0-08-097087-5</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. Not only is 'heuristic' used in diverse ways across and within disciplines, but its meaning has evolved over the years. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Kahneman & Frederick (2002) proposed that a heuristic assesses a target attribute by another property (attribute substitution) that comes more readily to mind. <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Gigerenzer, Gerd (2005). "I Think, Therefore I Err". Social Research. 72 (1): 195–218. doi:10.1353/sor.2005.0029. JSTOR 40972008. Retrieved 5 May 2024. A good error is a consequence of the adaptation of mental heuristics to the structure of environments. This ecological view is illustrated by visual illusions. Not making good errors would destroy human intelligence. <a href="http://www.jstor.org/stable/40972008" target="_blank">http://www.jstor.org/stable/40972008</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. Heuristics are commonly understood as economical shortcut procedures that may not lead to optimal or correct results, but will generally produce outcomes that are in some sense satisfactory or 'good enough'. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>Romanycia, M.; Pelletier, F. (1985). "What is a heuristic?". Computational Intelligence. 1 (1): 47–58. doi:10.1111/j.1467-8640.1985.tb00058.x. Retrieved 7 May 2024. Hence to paraphrase Polya, heuristic is a science of problem-solving behavior that focuses on plausible, provisional, useful, but fallible, mental operations for discovering solutions. <a href="https://doi.org/10.1111/j.1467-8640.1985.tb00058.x" target="_blank">https://doi.org/10.1111/j.1467-8640.1985.tb00058.x</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Shah & Oppenheimer (2008) proposed that all heuristics rely on effort reduction by one or more of the following: (a) examining fewer cues, (b) reducing the effort of retrieving cue values, (c) simplifying the weighting of cues, (d) integrating less information, and (e) examining fewer alternatives. <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>Myers, David G. (2010). Social psychology (Tenth ed.). New York, NY: McGraw-Hill. p. 94. ISBN 978-0-07337-066-8. OCLC 667213323. <a href="978-0-07337-066-8" target="_blank">978-0-07337-066-8</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>"Heuristics—Explanation and examples". Conceptually. Archived from the original on 21 December 2021. Retrieved 23 October 2019. <a href="https://conceptually.org/concepts/heuristics" target="_blank">https://conceptually.org/concepts/heuristics</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>Polya, George (1945). How to Solve It (PDF). Princeton, NJ: Princeton University Press. pp. 113, 114, 117, 132. ISBN 978-0-691-16407-6. Retrieved 10 May 2024. {{cite book}}: ISBN / Date incompatibility (help) <a href="978-0-691-16407-6" target="_blank">978-0-691-16407-6</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Heuristics are a subset of strategies; strategies also include complex regression or Bayesian models. <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. In a recent review article written with Wolfgang Gaissmaier, the following definition is proposed: <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. Another negative and substantial consequence was that computational models of heuristics, such as lexicographic rules (Fishburn, 1974) and elimination-by-aspects (Tversky, 1972), became replaced by one-word labels: availability, representativeness, and anchoring. <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></p></li> <li id="fn:17"><p>Pearl, Judea (1983). Heuristics: Intelligent Search Strategies for Computer Problem Solving. New York, NY: Addison-Wesley. p. vii. ISBN 978-0-201-05594-8. <a href="978-0-201-05594-8" target="_blank">978-0-201-05594-8</a> <a href="#fnref:17" class="footnote-back-ref">↩</a></p></li> <li id="fn:18"><p>Emiliano, Ippoliti (2015). Heuristic Reasoning: Studies in Applied Philosophy, Epistemology and Rational Ethics. Switzerland: Springer International Publishing. pp. 1–2. ISBN 978-3-319-09159-4. Archived from the original on 2019-07-11. Retrieved 2015-11-24. <a href="978-3-319-09159-4" target="_blank">978-3-319-09159-4</a> <a href="#fnref:18" class="footnote-back-ref">↩</a></p></li> <li id="fn:19"><p>Sunstein, Cass (2005). "Moral Heuristics". The Behavioral and Brain Sciences. 28 (4): 531–542. doi:10.1017/S0140525X05000099. PMID 16209802. S2CID 231738548. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:19" class="footnote-back-ref">↩</a></p></li> <li id="fn:20"><p>Hjeij, Mohamad; Vilks, Arnis (2023). "A brief history of heuristics: how did research on heuristics evolve?". Humanities and Social Sciences Communications. 10 (1): 1–15. doi:10.1057/s41599-023-01542-z. Gigerenzer (2021) [says] humans [and] other organisms evolved to acquire what he calls 'embodied heuristics' that can be both innate or learnt rules of thumb, which in turn supply the agility to respond to the lack of information by fast judgement. The 'embodied heuristics' use the mental capacity that includes the motor and sensory abilities that start to develop from the moment of birth. [...] 'dual-process theories' [...] we find it helpful to point out that one may distinguish between 'System 1 heuristics' [neuro] and 'System 2 heuristics' [neuro] (Kahneman 2011, p. 98). <a href="https://doi.org/10.1057%2Fs41599-023-01542-z" target="_blank">https://doi.org/10.1057%2Fs41599-023-01542-z</a> <a href="#fnref:20" class="footnote-back-ref">↩</a></p></li> <li id="fn:21"><p>Nickles, Thomas (1987). "Lakatosian Heuristics and Epistemic Support". The British Journal for the Philosophy of Science. 38 (2): 181–205. doi:10.1093/bjps/38.2.181. JSTOR 687047. Retrieved 5 May 2024. As Popperians and Lakatosians use the term, a 'justificationist' theory of knowledge is one committed to the existence of foundations of knowledge, at least probabilistic foundations. <a href="http://www.jstor.org/stable/687047" target="_blank">http://www.jstor.org/stable/687047</a> <a href="#fnref:21" class="footnote-back-ref">↩</a></p></li> <li id="fn:22"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. This stopping rule, termed a confirmation rule, works well in situations where (a) the decision maker knows little about the validity of the cues, and (b) the costs of cues are rather low (Karelaia, 2006). <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:22" class="footnote-back-ref">↩</a></p></li> <li id="fn:23"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. One-reason decisions: a class of heuristics that bases judgments on one good reason only, ignoring other cues (e.g., take-the-best and hiatus heuristic) <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:23" class="footnote-back-ref">↩</a></p></li> <li id="fn:24"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. Just as there is a class of such tracking heuristics, there is a class of one-good-reason heuristics, of which take-the-best is one member. These heuristics also have three building blocks: search rules, stopping rules, and decision rules. <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:24" class="footnote-back-ref">↩</a></p></li> <li id="fn:25"><p>Todd, P; Dieckmann, A (2004). "Heuristics for Ordering Cue Search in Decision Making". Advances in Neural Information Processing Systems: 13–18. Retrieved 6 May 2024. TTB consists of three building blocks. (1) Search rule: Search through cues in the order of their validity, a measure of accuracy equal to the proportion of correct decisions made by a cue out of all the times that cue discriminates between pairs of options. (2) Stopping rule: Stop search as soon as one cue is found that discriminates between the two options. (3) Decision rule: Select the option to which the discriminating cue points, that is, the option that has the cue value associated with higher criterion values. <a href="https://www.researchgate.net/publication/221620559" target="_blank">https://www.researchgate.net/publication/221620559</a> <a href="#fnref:25" class="footnote-back-ref">↩</a></p></li> <li id="fn:26"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Take the best (Gigerenzer & Goldstein, 1996). Infer which of two alternatives has the higher value by (a) searching through cues in order of validity, (b) stopping the search as soon as a cue discriminates, (c) choosing the alternative this cue favors. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:26" class="footnote-back-ref">↩</a></p></li> <li id="fn:27"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. Take-the-best is a member of the one-good-reason family of heuristics because of its stopping rule: Search is stopped after finding the first cue that enables an inference to be made. <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:27" class="footnote-back-ref">↩</a></p></li> <li id="fn:28"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Wubben & Wangenheim (2008) reported that experienced managers use a simple recency-of-last-purchase rule: 'Hiatus heuristic: If a customer has not purchased within a certain number of months (the hiatus), the customer is classified as inactive; otherwise, the customer is classified as active.' <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:28" class="footnote-back-ref">↩</a></p></li> <li id="fn:29"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Default heuristic (Johnson & Goldstein, 2003). If there is a default, do nothing about it. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:29" class="footnote-back-ref">↩</a></p></li> <li id="fn:30"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. The priority heuristic, a one-good-reason heuristic with no free parameters (Brandstätter, Gigerenzer, & Hertwig, 2008; Brandstätter et al., 2006) that has similar building blocks to take-the-best, has been shown to imply (not just have parameter sets that are consistent with) several of the major violations simultaneously, including the Allais paradox and the fourfold pattern (Katsikopoulos & Gigerenzer, 2008). <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:30" class="footnote-back-ref">↩</a></p></li> <li id="fn:31"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Johnson & Raab (2003) proposed a variant of the fluency heuristic when alternatives are sequentially retrieved rather than simultaneously perceived: 'Take-the-first heuristic: Choose the first alternative that comes to mind.' <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:31" class="footnote-back-ref">↩</a></p></li> <li id="fn:32"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Recognition-based decisions: a class of heuristics that bases judgments on recognition information only, ignoring other cues (e.g., recognition and fluency heuristic) <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:32" class="footnote-back-ref">↩</a></p></li> <li id="fn:33"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. For two alternatives, the heuristic is defined as (Goldstein & Gigerenzer 2002): 'Recognition heuristic: If one of two alternatives is recognized and the other is not, then infer that the recognized alternative has the higher value with respect to the criterion.' <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:33" class="footnote-back-ref">↩</a></p></li> <li id="fn:34"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Recognition heuristic (Goldstein & Gigerenzer, 2002). If one of two alternatives is recognized, infer that it has the higher value on the criterion. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:34" class="footnote-back-ref">↩</a></p></li> <li id="fn:35"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Fluency heuristic (Schooler & Hertwig, 2005). If one alternative is recognized faster than another, infer that it has the higher value on the criterion. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:35" class="footnote-back-ref">↩</a></p></li> <li id="fn:36"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. 'Fluency heuristic: If both alternatives are recognized but one is recognized faster, then infer that this alternative has the higher value with respect to the criterion.' The fluency heuristic builds on earlier work on fluency (Jacoby & Dallas 1981). <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:36" class="footnote-back-ref">↩</a></p></li> <li id="fn:37"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. The gaze heuristic introduced earlier has three building blocks. ... there is a class of such tracking heuristics ... <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:37" class="footnote-back-ref">↩</a></p></li> <li id="fn:38"><p>Gigerenzer, Gerd; Brighton, Henry (2009). "Homo Heuristicus: Why Biased Minds Make Better Inferences". Topics in Cognitive Science. 1 (1): 107–143. doi:10.1111/j.1756-8765.2008.01006.x. hdl:11858/00-001M-0000-0024-F678-0. PMID 25164802. Retrieved 6 May 2024. The gaze heuristic introduced earlier has three building blocks. ... there is a class of such tracking heuristics ... <a href="https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x" target="_blank">https://onlinelibrary.wiley.com/doi/10.1111/j.1756-8765.2008.01006.x</a> <a href="#fnref:38" class="footnote-back-ref">↩</a></p></li> <li id="fn:39"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Trade-offs: a class of heuristics that weights all cues or alternatives equally and thus makes trade-offs (e.g., tallying and 1/N) <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:39" class="footnote-back-ref">↩</a></p></li> <li id="fn:40"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Tallying (unit-weight linear model; Dawes, 1979). To estimate a criterion, do not estimate weights but simply count the number of favoring cues. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:40" class="footnote-back-ref">↩</a></p></li> <li id="fn:41"><p>Swire-Thompson, Briony; Ecker, Ullrich; Lewandowsky, Stephan; Berinsky, Adam (2020). "They Might Be a Liar But They're My Liar: Source Evaluation and the Prevalence of Misinformation". Political Psychology. 41: 21–34. doi:10.1111/pops.12586. hdl:1983/27f75033-2ac4-4249-b1cc-ae076b96f013. JSTOR 4529525. This also could be in accordance with the tallying heuristic where people count the number of arguments (for example, pros and cons) and disregard the relative importance of each argument (Bonnefon, Dubois, Fargier, & Leblois, 2008; Gigerenzer, 2004). <a href="http://www.jstor.org/stable/4529525" target="_blank">http://www.jstor.org/stable/4529525</a> <a href="#fnref:41" class="footnote-back-ref">↩</a></p></li> <li id="fn:42"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. 1/N; equality heuristic (DeMiguel et al., 2006). Allocate resources equally to each of N alternatives. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:42" class="footnote-back-ref">↩</a></p></li> <li id="fn:43"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. [Social heuristics] include imitation heuristics, tit-for-tat, the social-circle heuristic, and averaging the judgments of others to exploit the 'wisdom of crowds' (Hertwig & Herzog 2009). Imitate the-successful, for instance, speeds up learning of cue orders and can find orders that excel take-the-best's validity order (Garcia-Retamero et al. 2009). <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:43" class="footnote-back-ref">↩</a></p></li> <li id="fn:44"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Imitate the majority (Boyd & Richerson, 2005). Look at a majority of people in your peer group, and imitate their behavior. Imitate the successful (Boyd &Richerson, 2005). Look for the most successful person and imitate his or her behavior. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:44" class="footnote-back-ref">↩</a></p></li> <li id="fn:45"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Tit-for-tat (Axelrod, 1984). Cooperate first, keep a memory of Size 1, and then imitate your partner's last behavior. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:45" class="footnote-back-ref">↩</a></p></li> <li id="fn:46"><p>Mondak, Jeffery (1993). "Public Opinion and Heuristic Processing of Source Cues". Political Behavior. 15 (2): 167–92. doi:10.1007/BF00993852. JSTOR 586448. Retrieved 7 May 2024. [I]f a person believes that audience consensus usually offers accurate guidance as to the merits of persuasive messages, then positive audience reaction to a specific message would prompt the individual to accept the speaker's claims. The cognitive heuristic is the holding that audience consensus in this case is representative of situations in which audience consensus provides a reliable guide (Axsom, Yates, and Chaiken, 1987). <a href="http://www.jstor.org/stable/586448" target="_blank">http://www.jstor.org/stable/586448</a> <a href="#fnref:46" class="footnote-back-ref">↩</a></p></li> <li id="fn:47"><p>Charteris, Jennifer (2014). "Epistemological shudders as productive aporia: A heuristic for transformative teacher learning". International Journal of Qualitative Methods. 13 (1): 104–121. doi:10.1177/160940691401300102. Lozinski and Collinson (1999, as cited in Giugni, 2006) were the first to employ the concept of an 'epistemological shudder' to describe how one's preferred representations of one's known world can prove incapable of immediately making sense of the 'marvellous' (p. 101). <a href="https://doi.org/10.1177%2F160940691401300102" target="_blank">https://doi.org/10.1177%2F160940691401300102</a> <a href="#fnref:47" class="footnote-back-ref">↩</a></p></li> <li id="fn:48"><p>Krist, Christina; Schwarz, Christina; Reiser, Brian (2018). "Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning". Journal of the Learning Sciences. 28 (2): 160–205. doi:10.1080/10508406.2018.1510404. Retrieved 11 May 2024. The first epistemic heuristic essential to mechanistic reasoning is that students think across scalar levels. Most definitions of mechanistic reasoning (e.g., Grotzer & Perkins, 2000; Machamer et al., 2000) use the term underlying to describe the kinds of things that must be identified and characterized in order to explain a target phenomenon. <a href="https://doi.org/10.1080/10508406.2018.1510404" target="_blank">https://doi.org/10.1080/10508406.2018.1510404</a> <a href="#fnref:48" class="footnote-back-ref">↩</a></p></li> <li id="fn:49"><p>Krist, Christina; Schwarz, Christina; Reiser, Brian (2018). "Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning". Journal of the Learning Sciences. 28 (2): 160–205. doi:10.1080/10508406.2018.1510404. Retrieved 11 May 2024. second epistemic heuristic: identifying and characterizing relevant elements at a scalar level below that of the target phenomenon. ... we use the term factor to refer generally to the relevant elements at the scalar level below that of the aggregate phenomenon. Similarly, we refer generally to the intellectual work involved in characterizing the relevant properties, rules, and behaviors of factors as unpacking those factors. <a href="https://doi.org/10.1080/10508406.2018.1510404" target="_blank">https://doi.org/10.1080/10508406.2018.1510404</a> <a href="#fnref:49" class="footnote-back-ref">↩</a></p></li> <li id="fn:50"><p>Krist, Christina; Schwarz, Christina; Reiser, Brian (2018). "Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning". Journal of the Learning Sciences. 28 (2): 160–205. doi:10.1080/10508406.2018.1510404. Retrieved 11 May 2024. Finally, the third heuristic essential to mechanistic reasoning involves checking how well the underlying mechanisms fit the observed phenomenon. <a href="https://doi.org/10.1080/10508406.2018.1510404" target="_blank">https://doi.org/10.1080/10508406.2018.1510404</a> <a href="#fnref:50" class="footnote-back-ref">↩</a></p></li> <li id="fn:51"><p>Nouri, Pouria; Imanipour, Narges; Talebi, Kambiz; Zali, Mohammadreza (2018). "Most common heuristics and biases in nascent entrepreneurs' marketing behavior". Journal of Small Business & Entrepreneurship. 30 (6): 451–472. doi:10.1080/08276331.2018.1427406. Retrieved 11 May 2024. The affect heuristic is one of the most common heuristics in individuals, and has been a popular topic in the study of behavioral finance (Finucane et al. 2000). <a href="https://www.tandfonline.com/doi/abs/10.1080/08276331.2018.1427406" target="_blank">https://www.tandfonline.com/doi/abs/10.1080/08276331.2018.1427406</a> <a href="#fnref:51" class="footnote-back-ref">↩</a></p></li> <li id="fn:52"><p>Hart, Sergiu (2005). "Adaptive Heuristics". Econometrica. 73 (5): 1401–30. doi:10.1111/j.1468-0262.2005.00625.x. JSTOR 3598879. Retrieved 6 May 2024. Adaptive heuristics commonly appear in behavioral models, such as reinforcement, feedback, and stimulus-response. <a href="http://www.jstor.org/stable/3598879" target="_blank">http://www.jstor.org/stable/3598879</a> <a href="#fnref:52" class="footnote-back-ref">↩</a></p></li> <li id="fn:53"><p>Hart, Sergiu (2005). "Adaptive Heuristics". Econometrica. 73 (5): 1401–30. doi:10.1111/j.1468-0262.2005.00625.x. JSTOR 3598879. Retrieved 6 May 2024. Adaptive heuristics commonly appear in behavioral models, such as reinforcement, feedback, and stimulus-response. <a href="http://www.jstor.org/stable/3598879" target="_blank">http://www.jstor.org/stable/3598879</a> <a href="#fnref:53" class="footnote-back-ref">↩</a></p></li> <li id="fn:54"><p>Hart, Sergiu (2005). "Adaptive Heuristics". Econometrica. 73 (5): 1401–30. doi:10.1111/j.1468-0262.2005.00625.x. JSTOR 3598879. Retrieved 6 May 2024. Adaptive heuristics commonly appear in behavioral models, such as reinforcement, feedback, and stimulus-response. <a href="http://www.jstor.org/stable/3598879" target="_blank">http://www.jstor.org/stable/3598879</a> <a href="#fnref:54" class="footnote-back-ref">↩</a></p></li> <li id="fn:55"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. However, a different meaning of 'heuristic' was invoked in psychology with the Gestalt theorists, and later with Simon's notion of 'satisficing'. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:55" class="footnote-back-ref">↩</a></p></li> <li id="fn:56"><p>Gigerenzer, Gerd (2008). "Why Heuristics Work". Perspectives on Psychological Science. 3 (1): 20–29. doi:10.1111/j.1745-6916.2008.00058.x. JSTOR 40212224. PMID 26158666. Retrieved 5 May 2024. Satisficing (Simon, 1955; Todd & Miller, 1999). Search through alternatives, and choose the first one that exceeds your aspiration level. <a href="http://www.jstor.org/stable/40212224" target="_blank">http://www.jstor.org/stable/40212224</a> <a href="#fnref:56" class="footnote-back-ref">↩</a></p></li> <li id="fn:57"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Simon's (1955) satisficing heuristic searches through options in any order, stops as soon the first option exceeds an aspiration level, and chooses this option. <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:57" class="footnote-back-ref">↩</a></p></li> <li id="fn:58"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [T]he representativeness heuristic[:]Probabilities are evaluated by the degree to which one thing or event is representative of (resembles) another; the higher the representativeness (resemblance) the higher the probability estimation[.] <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:58" class="footnote-back-ref">↩</a></p></li> <li id="fn:59"><p>Lu, Yun; Vasko, Francis; Drummond, Trevor; Vasko, Lisa (2014). "Probability & Perception: The Representativeness Heuristic in Action". The Mathematics Teacher. 108 (2): 126–31. doi:10.5951/mathteacher.108.2.0126. Retrieved 5 May 2024. The belief that a sequence such as 11111111111111111111 is less probable than a sequence such as 66234441536125563152 is often referred to as the representativeness heuristic (Kahneman and Tversky 1972; Shaughnessy 1977, 1992). <a href="https://doi.org/10.5951/mathteacher.108.2.0126" target="_blank">https://doi.org/10.5951/mathteacher.108.2.0126</a> <a href="#fnref:59" class="footnote-back-ref">↩</a></p></li> <li id="fn:60"><p>Kahneman, Daniel; Tversky, Amos (July 1973). "On the psychology of prediction". Psychological Review. 80 (4): 237–251. doi:10.1037/h0034747. ISSN 1939-1471. Archived from the original on 2023-10-28. Retrieved 2023-05-09. <a href="http://doi.apa.org/getdoi.cfm?doi=10.1037/h0034747" target="_blank">http://doi.apa.org/getdoi.cfm?doi=10.1037/h0034747</a> <a href="#fnref:60" class="footnote-back-ref">↩</a></p></li> <li id="fn:61"><p>Tversky, Amos; Kahneman, Daniel (1973-09-01). "Availability: A heuristic for judging frequency and probability". Cognitive Psychology. 5 (2): 207–232. doi:10.1016/0010-0285(73)90033-9. ISSN 0010-0285. Archived from the original on 2023-10-28. Retrieved 2023-08-24. <a href="https://dx.doi.org/10.1016/0010-0285%2873%2990033-9" target="_blank">https://dx.doi.org/10.1016/0010-0285%2873%2990033-9</a> <a href="#fnref:61" class="footnote-back-ref">↩</a></p></li> <li id="fn:62"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [T]he availability heuristic[:]The frequency of a class or the probability of an event is assessed according to the ease with which instances or associations can be brought to mind (Tversky and Kahneman [1974]) <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:62" class="footnote-back-ref">↩</a></p></li> <li id="fn:63"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. Max Wertheimer, who was a close friend of Einstein, and his fellow Gestalt psychologists spoke of heuristic methods such as 'looking around' to guide search for information. <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:63" class="footnote-back-ref">↩</a></p></li> <li id="fn:64"><p>Wacquant, Loic (1985). "Heuristic Models in Marxian Theory". Social Forces. 64 (1): 17–45. doi:10.2307/2578970. JSTOR 2578970. Retrieved 6 May 2024. In building social theory, Marx used not one (as generally regarded) but three heuristic models: base-superstructure, organic totality, and dialectical development. <a href="https://doi.org/10.2307/2578970" target="_blank">https://doi.org/10.2307/2578970</a> <a href="#fnref:64" class="footnote-back-ref">↩</a></p></li> <li id="fn:65"><p>Wacquant, Loic (1985). "Heuristic Models in Marxian Theory". Social Forces. 64 (1): 17–45. doi:10.2307/2578970. JSTOR 2578970. Retrieved 6 May 2024. In building social theory, Marx used not one (as generally regarded) but three heuristic models: base-superstructure, organic totality, and dialectical development. <a href="https://doi.org/10.2307/2578970" target="_blank">https://doi.org/10.2307/2578970</a> <a href="#fnref:65" class="footnote-back-ref">↩</a></p></li> <li id="fn:66"><p>Wacquant, Loic (1985). "Heuristic Models in Marxian Theory". Social Forces. 64 (1): 17–45. doi:10.2307/2578970. JSTOR 2578970. Retrieved 6 May 2024. In building social theory, Marx used not one (as generally regarded) but three heuristic models: base-superstructure, organic totality, and dialectical development. <a href="https://doi.org/10.2307/2578970" target="_blank">https://doi.org/10.2307/2578970</a> <a href="#fnref:66" class="footnote-back-ref">↩</a></p></li> <li id="fn:67"><p>Hey, Spencer (2016). "Heuristics and Meta-Heuristics in Scientific Judgement". The British Journal for the Philosophy of Science. 67 (2): 471–95. doi:10.1093/bjps/axu045. JSTOR 43946078. Retrieved 5 May 2024. The continuum limit heuristic is one member of a more general class of heuristics for variable reduction (Wilson [2007], pp. 184–92). <a href="http://www.jstor.org/stable/43946078" target="_blank">http://www.jstor.org/stable/43946078</a> <a href="#fnref:67" class="footnote-back-ref">↩</a></p></li> <li id="fn:68"><p>Petersen, Michael (2015). "Evolutionary Political Psychology: On the Origin and Structure of Heuristics and Biases in Politics". Political Psychology. 36 (1): 45–78. doi:10.1111/pops.12237. JSTOR 43783844. Retrieved 5 May 2024. One of the political heuristics that has been most studied from an evolutionary perspective is the deservingness heuristic. ... the deservingness heuristic is the psychological tendency of people to base their opinions about welfare programs on the efforts of the recipients. Specifically, the heuristic motivates people to support welfare benefits to recipients who are represented as victims of bad luck and reject benefits to recipients who are represented as lazy. <a href="http://www.jstor.org/stable/43783844" target="_blank">http://www.jstor.org/stable/43783844</a> <a href="#fnref:68" class="footnote-back-ref">↩</a></p></li> <li id="fn:69"><p>Todd, P; Dieckmann, A (2004). "Heuristics for Ordering Cue Search in Decision Making". Advances in Neural Information Processing Systems: 13–18. Retrieved 6 May 2024. The even simpler Minimalist heuristic, which searches through available cues in a random order[.] <a href="https://www.researchgate.net/publication/221620559" target="_blank">https://www.researchgate.net/publication/221620559</a> <a href="#fnref:69" class="footnote-back-ref">↩</a></p></li> <li id="fn:70"><p>Kao, Molly (2019). "Unification beyond Justification: A Strategy for Theory Development". Synthese. 196 (8): 3263–78. doi:10.1007/s11229-017-1515-8. JSTOR 45215151. The focus on unification as a heuristic strategy parallels certain elements of a related type of reasoning, namely that found in robustness analysis. <a href="http://www.jstor.org/stable/45215151" target="_blank">http://www.jstor.org/stable/45215151</a> <a href="#fnref:70" class="footnote-back-ref">↩</a></p></li> <li id="fn:71"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:71" class="footnote-back-ref">↩</a></p></li> <li id="fn:72"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:72" class="footnote-back-ref">↩</a></p></li> <li id="fn:73"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:73" class="footnote-back-ref">↩</a></p></li> <li id="fn:74"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:74" class="footnote-back-ref">↩</a></p></li> <li id="fn:75"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:75" class="footnote-back-ref">↩</a></p></li> <li id="fn:76"><p>Schoemaker, Paul (1991). "The quest for optimality: A positive heuristic of science?". Behavioral and Brain Sciences. 14 (2): 205–245. doi:10.1017/S0140525X00066140. Retrieved 27 July 2024. As with any heuristic (Tversky & Kahneman 1974), however, the optimality approach is prone to systematic biases [...] 1. Posing a why question ...2. Bounding the domain of inquiry ...3. Selection of salient features ...4. Teleological description of the system ...5. Search for the optimal solution ...6. Empirical comparisons ...7. Further refinement of the model ...8. Generation of new hypotheses [...] Survival of the fittest, which is perhaps the grandest of all optimality principles, was formulated as a qualitative, conceptual cornerstone in Darwin's (1859) theory of evolution. Entropy and least action principles are other broad optimality laws [...] Equilibrium notions and homeostatic behavior can also be interpreted as general optimality principles, covering wide domains of application. <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F" target="_blank">https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/the-quest-for-optimality-a-positive-heuristic-of-science/0A56CEFA30BC66B72B121C80487A5F3F</a> <a href="#fnref:76" class="footnote-back-ref">↩</a></p></li> <li id="fn:77"><p>Romanycia, M.; Pelletier, F. (1985). "What is a heuristic?". Computational Intelligence. 1 (1): 47–58. doi:10.1111/j.1467-8640.1985.tb00058.x. Retrieved 7 May 2024. Minsky's (1961 b) subject bibliography lists Polya (1945) as the earliest reference to heuristic in the AI literature. <a href="https://doi.org/10.1111/j.1467-8640.1985.tb00058.x" target="_blank">https://doi.org/10.1111/j.1467-8640.1985.tb00058.x</a> <a href="#fnref:77" class="footnote-back-ref">↩</a></p></li> <li id="fn:78"><p>Polya, George (1945). How to Solve It (PDF). Princeton, NJ: Princeton University Press. p. 141. ISBN 978-0-691-16407-6. Retrieved 10 May 2024. {{cite book}}: ISBN / Date incompatibility (help) <a href="978-0-691-16407-6" target="_blank">978-0-691-16407-6</a> <a href="#fnref:78" class="footnote-back-ref">↩</a></p></li> <li id="fn:79"><p>Groner, Rudolf; Groner, Marina; Bischof, Walter (2014). Methods of heuristics. Routledge. The methods of analysis and synthesis appear later in almost every treatise on problem-solving methods [from Pappus]. <a href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=76a213b007a1c8072760b59b58df40729fa1f3c1" target="_blank">https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=76a213b007a1c8072760b59b58df40729fa1f3c1</a> <a href="#fnref:79" class="footnote-back-ref">↩</a></p></li> <li id="fn:80"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:80" class="footnote-back-ref">↩</a></p></li> <li id="fn:81"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. The most important work in heuristic teaching has been done by George Polya. His How To Solve It has been a best seller since its first printing in 1945-copies sold number in the hundreds of thousands. Complementary to How To Solve It are two other works, each in two volumes: Mathematical Discovery and Mathematics And Plausible Reasoning. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:81" class="footnote-back-ref">↩</a></p></li> <li id="fn:82"><p>Romanycia, M.; Pelletier, F. (1985). "What is a heuristic?". Computational Intelligence. 1 (1): 47–58. doi:10.1111/j.1467-8640.1985.tb00058.x. Retrieved 7 May 2024. Minsky's (1961 b) subject bibliography lists Polya (1945) as the earliest reference to heuristic in the AI literature. <a href="https://doi.org/10.1111/j.1467-8640.1985.tb00058.x" target="_blank">https://doi.org/10.1111/j.1467-8640.1985.tb00058.x</a> <a href="#fnref:82" class="footnote-back-ref">↩</a></p></li> <li id="fn:83"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:83" class="footnote-back-ref">↩</a></p></li> <li id="fn:84"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:84" class="footnote-back-ref">↩</a></p></li> <li id="fn:85"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:85" class="footnote-back-ref">↩</a></p></li> <li id="fn:86"><p>Hey, Spencer (2016). "Heuristics and Meta-Heuristics in Scientific Judgement". The British Journal for the Philosophy of Science. 67 (2): 471–95. doi:10.1093/bjps/axu045. JSTOR 43946078. Retrieved 5 May 2024. It is difficult to overstate the influence of Tversky and Kahneman's work and the so-called 'heuristics-and-biases research programme' that followed. <a href="http://www.jstor.org/stable/43946078" target="_blank">http://www.jstor.org/stable/43946078</a> <a href="#fnref:86" class="footnote-back-ref">↩</a></p></li> <li id="fn:87"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. [I]nfluential heuristics researchers, including George Polya, Herbert Simon, Daniel Kahneman and Amos Tversky, and Gerd Gigerenzer. <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:87" class="footnote-back-ref">↩</a></p></li> <li id="fn:88"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. 'To choose a ripe cantaloupe, press the spot on the candidate cantaloupe where it was attached to the plant and smell it; if the spot smells like the inside of a cantaloupe, it's probably ripe' (Pearl [1984]) <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:88" class="footnote-back-ref">↩</a></p></li> <li id="fn:89"><p>Chow, Sheldon (2015). "Many Meanings of 'Heuristic'". The British Journal for the Philosophy of Science. 66 (4): 977–1016. doi:10.1093/bjps/axu028. JSTOR 24562967. Retrieved 5 May 2024. 'Start in the centre square when beginning a game of tic-tac-toe' (Dunbar [1998]) <a href="http://www.jstor.org/stable/24562967" target="_blank">http://www.jstor.org/stable/24562967</a> <a href="#fnref:89" class="footnote-back-ref">↩</a></p></li> <li id="fn:90"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:90" class="footnote-back-ref">↩</a></p></li> <li id="fn:91"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:91" class="footnote-back-ref">↩</a></p></li> <li id="fn:92"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:92" class="footnote-back-ref">↩</a></p></li> <li id="fn:93"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:93" class="footnote-back-ref">↩</a></p></li> <li id="fn:94"><p>Zalcman, Lawrence (1975). "A Heuristic Principle in Complex Function Theory". The American Mathematical Monthly. 82 (8): 813–18. doi:10.2307/2319796. JSTOR 2319796. Retrieved 5 May 2024. <a href="https://doi.org/10.2307/2319796" target="_blank">https://doi.org/10.2307/2319796</a> <a href="#fnref:94" class="footnote-back-ref">↩</a></p></li> <li id="fn:95"><p>Hey, Spencer (2016). "Heuristics and Meta-Heuristics in Scientific Judgement". The British Journal for the Philosophy of Science. 67 (2): 471–95. doi:10.1093/bjps/axu045. JSTOR 43946078. Retrieved 5 May 2024. Lakatos ([1965]) also adopted the term to characterize his methodology of scientific research programmes, which would lead researchers to either avoid or pursue certain lines of inquiry 'negative' and 'positive' heuristics, respectively). <a href="http://www.jstor.org/stable/43946078" target="_blank">http://www.jstor.org/stable/43946078</a> <a href="#fnref:95" class="footnote-back-ref">↩</a></p></li> <li id="fn:96"><p>Hey, Spencer (2016). "Heuristics and Meta-Heuristics in Scientific Judgement". The British Journal for the Philosophy of Science. 67 (2): 471–95. doi:10.1093/bjps/axu045. JSTOR 43946078. Retrieved 5 May 2024. Wimsatt's ([1980], [1981], [2006], [2007]) work on reductionist modelling strategies - also built upon Simon's programme of bounded rationality - provides an alternative starting point that is more useful for understanding the role that heuristics play in science. <a href="http://www.jstor.org/stable/43946078" target="_blank">http://www.jstor.org/stable/43946078</a> <a href="#fnref:96" class="footnote-back-ref">↩</a></p></li> <li id="fn:97"><p>Schaffner, Kenneth (2008). "Theories, Models, and Equations in Biology: The Heuristic Search for Emergent Simplifications in Neurobiology". Philosophy of Science. 75 (5): 1008–21. doi:10.1086/594542. Retrieved 5 May 2024. In a series of papers beginning in 1980 and represented in his 2007 book, Bill Wimsatt analyzed a series of 'heuristics,' thought of as guides or 'rules of thumb,' which are employed when scientists proceed in a reductionist manner (1980, 2007). <a href="https://doi.org/10.1086/594542" target="_blank">https://doi.org/10.1086/594542</a> <a href="#fnref:97" class="footnote-back-ref">↩</a></p></li> <li id="fn:98"><p>Schaffner, Kenneth (2008). "Theories, Models, and Equations in Biology: The Heuristic Search for Emergent Simplifications in Neurobiology". Philosophy of Science. 75 (5): 1008–21. doi:10.1086/594542. Retrieved 5 May 2024. In summary, Hodgkin and Huxley use heuristics in the Wimsatt sense, and the heuristics fall both into what Wimsatt calls reductionistic heuristics and also nonreductionistic heuristics. <a href="https://doi.org/10.1086/594542" target="_blank">https://doi.org/10.1086/594542</a> <a href="#fnref:98" class="footnote-back-ref">↩</a></p></li> <li id="fn:99"><p>Schaffner, Kenneth (2008). "Theories, Models, and Equations in Biology: The Heuristic Search for Emergent Simplifications in Neurobiology". Philosophy of Science. 75 (5): 1008–21. doi:10.1086/594542. Retrieved 5 May 2024. In summary, Hodgkin and Huxley use heuristics in the Wimsatt sense, and the heuristics fall both into what Wimsatt calls reductionistic heuristics and also nonreductionistic heuristics. <a href="https://doi.org/10.1086/594542" target="_blank">https://doi.org/10.1086/594542</a> <a href="#fnref:99" class="footnote-back-ref">↩</a></p></li> <li id="fn:100"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. Mauritz Johnson (1966) observes that the idea is hardly new, and that, ignoring the classical accreditation of its use to Socrates in the Meno, one finds an early discussion of discovery learning by David P. Page in his Theory and Practice of Teaching in 1847 as well as by later writers, Herbert Spencer in 1860, Frank and Charles McMurry in 1897, and William Chandler Babley in 1905. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:100" class="footnote-back-ref">↩</a></p></li> <li id="fn:101"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. The most important work in heuristic teaching has been done by George Polya. His How To Solve It has been a best seller since its first printing in 1945-copies sold number in the hundreds of thousands. Complementary to How To Solve It are two other works, each in two volumes: Mathematical Discovery and Mathematics And Plausible Reasoning. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:101" class="footnote-back-ref">↩</a></p></li> <li id="fn:102"><p>Hughes, Barnabas (1974). "Heuristic Teaching in Mathematics". Educational Studies in Mathematics. 5 (3): 291–99. doi:10.1007/BF00684704. JSTOR 3482053. Retrieved 5 May 2024. The most important work in heuristic teaching has been done by George Polya. His How To Solve It has been a best seller since its first printing in 1945-copies sold number in the hundreds of thousands. Complementary to How To Solve It are two other works, each in two volumes: Mathematical Discovery and Mathematics And Plausible Reasoning. <a href="http://www.jstor.org/stable/3482053" target="_blank">http://www.jstor.org/stable/3482053</a> <a href="#fnref:102" class="footnote-back-ref">↩</a></p></li> <li id="fn:103"><p>Kahneman, Daniel; Slovic, Paul; Tversky, Amos, eds. (30 April 1982). Judgment Under Uncertainty. Cambridge, UK: Cambridge University Press. doi:10.1017/cbo9780511809477. ISBN 978-0-52128-414-1. <a href="978-0-52128-414-1" target="_blank">978-0-52128-414-1</a> <a href="#fnref:103" class="footnote-back-ref">↩</a></p></li> <li id="fn:104"><p>"Heuristics and heuristic evaluation". The Glossary of Human Computer Interaction. Interaction Design Foundation. Archived from the original on 5 July 2015. Retrieved 1 September 2013. <a href="http://www.interaction-design.org/encyclopedia/heuristics_and_heuristic_evaluation.html" target="_blank">http://www.interaction-design.org/encyclopedia/heuristics_and_heuristic_evaluation.html</a> <a href="#fnref:104" class="footnote-back-ref">↩</a></p></li> <li id="fn:105"><p>Groner, Rudolf; Groner, Marina; Bischof, Walter F. (1983). Methods of Heuristics. Hillsdale, NJ: Lawrence Erlbaum. <a href="/wiki/Rudolf_Groner" target="_blank">/wiki/Rudolf_Groner</a> <a href="#fnref:105" class="footnote-back-ref">↩</a></p></li> <li id="fn:106"><p>Groner, Rudolf; Groner, Marina (1991). "Heuristische versus algorithmische Orientierung als Dimension des individuellen kognitiven Stils" [Heuristic versus algorithmic orientation as a dimension of the individual cognitive style]. In K. Grawe; N. Semmer; R. Hänni (eds.). Über die richtige Art, Psychologie zu betreiben [About the right way to do psychology] (in German). Göttingen: Hogrefe. ISBN 978-3-80170-415-5. <a href="978-3-80170-415-5" target="_blank">978-3-80170-415-5</a> <a href="#fnref:106" class="footnote-back-ref">↩</a></p></li> <li id="fn:107"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". Annual Review of Psychology. 62: 451–482. doi:10.1146/annurev-psych-120709-145346. hdl:11858/00-001M-0000-0024-F16D-5. PMID 21126183. Retrieved 6 May 2024. The collection of heuristics and building blocks an individual or a species has at its disposal for constructing heuristics, together with the core mental capacities that building blocks exploit, has been called the adaptive toolbox (Gigerenzer et al. 1999). <a href="https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content" target="_blank">https://pure.mpg.de/rest/items/item_2099042_4/component/file_2099041/content</a> <a href="#fnref:107" class="footnote-back-ref">↩</a></p></li> <li id="fn:108"><p>Gigerenzer, G.; Gaissmaier, W. (2011). "Heuristic Decision Making". 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