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  3. 2022
  4. Preliminary Program
  5. Cognitive Mechanisms: Theory and Evidence
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Cognitive Mechanisms: Theory and Evidence

Paper Session

Sunday, Jan. 9, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: Econometric Society
  • Chair: Muriel Niederle, Stanford University

Expectation Conformity in Strategic Cognition

Jean Tirole
,
Toulouse School of Economics
Alessandro Pavan
,
Northwestern University

Abstract

The paper studies “cognitive games,” that is, games in which the players can influence their understanding of a strategic situation before playing the primitive (normal- or extensive-form) game. The analysis covers both the case of self-directed cognition (as when a player controls her own information structure) and the case of manipulative cognition (as when a player influences her opponents’ understanding of the game). We introduce the concept of expectation conformity and show how the latter, together with its decomposition into unilateral expectation conformity and increasing differences, sheds light on the choice of the cognitive structures (both on and off the equilibrium path) and on the sensitivity of the cognitive postures to the type of strategic interaction (e.g., complements vs substitutes). We show that constant-sum games never give rise to self-fulfilling cognition. By contrast, the latter emerges in many non-constant-sum games, both when cognition is self-directed and takes the form of “sparsity,” noisy information acquisition, or “espionage,” (i.e., learning about others’ beliefs), and when it is manipulative and takes the form of framing, signal jamming, noisy disclosures, and counter-intelligence. Finally, we discuss the role that expectation conformity plays in games with boundedly-rational players such as those considered in the level-k literature.

The Inference-Forecast Gap in Belief Updating

Tony Fan
,
Stanford University
Yucheng Liang
,
briq Institute
Cameron Peng
,
London School of Economics

Abstract

Individual forecasts of economic variables show widespread overreaction to news, but laboratory experiments on belief updating typically find underinference from signals. We provide new experimental evidence to connect these two seemingly inconsistent phenomena. Building on a classic experimental paradigm, we study how people make inferences and revise forecasts in the same information environment. Subjects underreact to signals when inferring about underlying states, but overreact to signals when revising forecasts about future outcomes. This gap in belief updating is largely driven by the use of different simplifying heuristics for the two tasks. Additional treatments link our results to the difficulty of recognizing the conceptual connection between making inferences and revising forecasts.

Subjective Causality in Choice

Andrew Ellis
,
London School of Economics
Heidi Thysen
,
London School of Economics

Abstract

An agent makes a stochastic choice from a set of lotteries. She infers the outcomes of her options using a subjective causal model represented by a directed acyclic graph, and consequently may misinterpret correlation as causality. Her choices affect her inferences which in turn affect her choices, so the two together must form a personal equilibrium. We show how an analyst can identify the agent's subjective causal model from her random choice rule. In addition, we provide necessary and sufficient conditions that allow an analyst to test whether the agent's behavior is compatible with the model.

Mental Models and Learning: The Case of Base-Rate Neglect

Ignacio Esponda
,
University of California-Santa Barbara
Emanuel Vespa
,
University of California-San Diego
Sevgi Yuksel
,
University of California-Santa Barbara

Abstract

We study whether suboptimal behavior can persist in the presence of feedback and examine the role that incorrect mental models play in this persistence. Focusing on a simple updating problem, we document using a laboratory experiment the evolution of beliefs in response to feedback. We compare a baseline treatment, in which a majority of subjects display base-rate neglect (BRN) in initial beliefs, to a control treatment that does not allow for BRN as a mental model but in which learning from feedback is similarly possible. Learning is slow and partial in the baseline, such that after 200 rounds of feedback, beliefs in this treatment are farther from the Bayesian benchmark relative to the control treatment. The treatment effect is linked to partial attentiveness to feedback by those subjects who initially display BRN in the baseline. Presenting subjects with evidence that unequivocally challenges their beliefs by summarizing feedback up to that point improves the accuracy of beliefs substantially and eliminates base-rate neglect. Finally, we find evidence that learning from feedback can generate insights (for example, that the base-rate should be considered in the belief formation process) that can be partially transferred to new settings.
JEL Classifications
  • D91 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making