Analogies and theories: The role of simplicity and the emergence of norms

Gayer, Gabrielle; Gilboa, Itzhak

doi:10.1016/j.geb.2013.11.003

Cited by 5 publications

(6 citation statements)

References 22 publications

(10 reference statements)

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“…Neuroeconomics is also in agreement with many other learning models. It is hypothesized by Gayer and Gilboa [20] that, in simple games, case-based reasoning is more likely to be discarded in favor of rule-based reasoning, but case-based reasoning is likely to remain in complex games. CBDT is related to the learning model of Bordalo et al [21], which uses a similarity measure to determine which past experiences are recalled from memory.…”

Section: Introductionmentioning

confidence: 99%

Estimating Case-Based Learning

Guilfoos

Pape

2020

Games

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We propose a framework in order to econometrically estimate case-based learning and apply it to empirical data from twelve 2 × 2 mixed strategy equilibria experiments. Case-based learning allows agents to explicitly incorporate information available to the experimental subjects in a simple, compact, and arguably natural way. We compare the estimates of case-based learning to other learning models (reinforcement learning and self-tuned experience weighted attraction learning) while using in-sample and out-of-sample measures. We find evidence that case-based learning explains these data better than the other models based on both in-sample and out-of-sample measures. Additionally, the case-based specification estimates how factors determine the salience of past experiences for the agents. We find that, in constant sum games, opposing players’ behavior is more important than recency and, in non-constant sum games, the reverse is true.

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Section: Introductionmentioning

confidence: 99%

Estimating Case-Based Learning

Guilfoos

Pape

2020

Games

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“…Shouldn't the theory be directly tested based on its predictions, their fit to reality, and so forth? 11 While there are many reasons to be interested in axiomatic derivations of behavioral models, we hold that the case-based view of economic theory explains the interest in axiomatizations better than does the rule-based view. Consider a simple, textbook example.…”

Section: Axiomatizationsmentioning

confidence: 99%

Economic Models as Analogies, Second Version

et al. 2012

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People often wonder why economists analyze models whose assumptions are known to be false, while economists feel that they learn a great deal from such exercises. We suggest that part of the knowledge generated by academic economists is case-based rather than rule-based. That is, instead of offering general rules or theories that should be contrasted with data, economists often analyze models that are "theoretical cases", which help understand economic problems by drawing analogies between the model and the problem. According to this view, economic models, empirical data, experimental results and other sources of knowledge are all on equal footing, that is, they all provide cases to which a given problem can be compared. We offer complexity arguments that explain why case-based reasoning may sometimes be the method of choice; why economists prefer simple examples; and why a paradigm may be useful even if it does not produce theories. * We are thankful to many colleagues and friends with whom we have discussed the issues addressed here over the years. It is practically impossible to recall which ideas were suggested by whom, and we apologize for any inadvertent plagiarism. We thank Hervé Crès, Robin Cubitt, Eddie Dekel, Brian Hill, Doron Ravid, Jack Vromen, and Bernard Walliser for comments on earlier drafts of this paper. We gratefully acknowledges ISF Grant 396/10 (Gilboa and Schmeidler), ERC Grant 269754 (Gilboa), NSF Grants SES-0961540 (Postlewaite) and SES-0850263 (Samuelson).

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“…Gayer and Gilboa [2014] use a similar approach to compare rule-based and casebased conjectures. Rules correspond to deterministic theories in the language of Gilboa and Samuelson [2012] and thus make a prediction for every period.…”

Section: Cases Versus Rulesmentioning

confidence: 99%

Case-Based Decision Theory: From the Choice of Actions to Reasoning about Theories

Eichberger¹,

Guerdjikova²

2020

Revue Économique

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In the 1990's David Schmeidler and Itzhak Gilboa initiated the study of decision making under uncertainty in a completely new framework, without states but with data sets as the information on which to build choice behavior. While the first formulations of Case-Based Decision Theory (CBDT) aimed at applications in economic decision making, this theory which takes data as a primitive concept provides an alternative foundation for deriving beliefs and driving the choice of predictions. This opened a new perspective on old questions in statistics and artificial intelligence. In this review, we summarize these developments in Case-Based Decision Theory and highlight the immensely innovative nature of David Schmeidler's academic work.Dans les années 1990's David Schmeidler et Itzhak Gilboa ont introduit un nouvel cadre d'analyse des décisions sous incertitude: les bases des données se substituent aux états du monde comme primitive du modèle et informent le choix du décideur. Au début la théorie de décision au cas-parcas était orientée principalement vers des applications économiques, mais ses méthodes se sont avérées aussi pertinentes pour l'analyse des croyances et des prédictions statistiques. Ceci a ouvert des nouvelles perspectives sur des questions classiques en statistique et en intelligence artificielle. Dans cet article, nous passons en revue ces développements et mettons en avance le caractère extrêmement novateur des travaux académiques de David Schmeidler.

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Analogies and theories: The role of simplicity and the emergence of norms

Cited by 5 publications

References 22 publications

Estimating Case-Based Learning

Estimating Case-Based Learning

Economic Models as Analogies, Second Version

Case-Based Decision Theory: From the Choice of Actions to Reasoning about Theories

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