nep-neu New Economics Papers
on Neuroeconomics
Issue of 2021‒07‒19
three papers chosen by

  1. The Roots of Cooperation By Zvonimir Basic; Parampreet Christopher Bindra; Daniela Glätzle-Rützler; Angelo Romano; Matthias Sutter; Claudia Zoller
  2. Expl(AI)ned: The impact of explainable artificial intelligence on cognitive processes By Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver
  3. Limited Self-knowledge and Survey Response Behavior By Armin Falk; Thomas Neuber; Philipp Strack

  1. By: Zvonimir Basic; Parampreet Christopher Bindra; Daniela Glätzle-Rützler; Angelo Romano; Matthias Sutter; Claudia Zoller
    Abstract: Understanding the roots of human cooperation among strangers is of great importance for solving pressing social dilemmas and maintening public goods in human societies. We study the development of cooperation in 929 young children, aged 3 to 6. In a unified experimental framework, we examine which of three fundamental pillars of human cooperation – direct and indirect reciprocity as well as third-party punishment – emerges earliest as an effective means to increase cooperation in a repeated prisoner’s dilemma game. We find that third-party punishment exhibits a strikingly positive effect on cooperation rates by doubling them in comparison to a control condition. It promotes cooperative behavior even before punishment of defectors is applied. Children also engage in reciprocating others, showing that reciprocity strategies are already prevalent at a very young age. However, direct and indirect reciprocity treatments do not increase overall cooperation rates, as young children fail to anticipate the benefits of reputation building. We also show that the cognitive skills of children and the socioeconomic background of parents play a vital role in the early development of human cooperation.
    Keywords: cooperation, reciprocity, third-party punishment, reputation, children, parents, cognitive abilities, socioeconomic status, prisoner’s dilemma game, experiment
    JEL: C91 C93 D01 D91 H41
    Date: 2021
  2. By: Bauer, Kevin; von Zahn, Moritz; Hinz, Oliver
    Abstract: This paper explores the interplay of feature-based explainable AI (XAI) techniques, information processing, and human beliefs. Using a novel experimental protocol, we study the impact of providing users with explanations about how an AI system weighs inputted information to produce individual predictions (LIME) on users' weighting of information and beliefs about the task-relevance of information. On the one hand, we find that feature-based explanations cause users to alter their mental weighting of available information according to observed explanations. On the other hand, explanations lead to asymmetric belief adjustments that we interpret as a manifestation of the confirmation bias. Trust in the prediction accuracy plays an important moderating role for XAI-enabled belief adjustments. Our results show that feature-based XAI does not only superficially influence decisions but really change internal cognitive processes, bearing the potential to manipulate human beliefs and reinforce stereotypes. Hence, the current regulatory efforts that aim at enhancing algorithmic transparency may benefit from going hand in hand with measures ensuring the exclusion of sensitive personal information in XAI systems. Overall, our findings put assertions that XAI is the silver bullet solving all of AI systems' (black box) problems into perspective.
    Keywords: XAI,explainable machine learning,Information Processing,Belief updating,algorithmic transparency
    Date: 2021
  3. By: Armin Falk (briq and the University of Bonn); Thomas Neuber (University of Bonn); Philipp Strack (Yale University)
    Abstract: We study response behavior in surveys and show how the explanatory power of self-reports can be improved. First, we develop a choice model of survey response behavior under the assumption that the respondent has imperfect self-knowledge about her individual characteristics. In panel data, the model predicts that the variance in responses for different characteristics increases in self-knowledge and that the variance for a given characteristic over time is non-monotonic in self-knowledge. Importantly, the ratio of these variances identifies an individual's level of self-knowledge, i.e. the latter can be inferred from observed response patterns. Second, we develop a consistent and unbiased estimator for self-knowledge based on the model. Third, we run an experiment to test the model's main predictions in a context where the researcher knows the true underlying characteristics. The data confirm the model's predictions as well as the estimator's validity. Finally, we turn to a large panel data set, estimate individual levels of self-knowledge, and show that accounting for differences in self-knowledge significantly increases the explanatory power of regression models. Using a median split in self-knowledge and regressing risky behaviors on self-reported risk attitudes, we find that the R2 can be multiple times larger for above- than below-median subjects. Similarly, gender differences in risk attitudes are considerably larger when restricting samples to subjects with high self-knowledge. These examples illustrate how using the estimator may improve inference from survey data.
    Keywords: survey research, rational inattention, laboratory experiments, non-cognitive skills, preferences
    JEL: C83 D91 J24
    Date: 2021–07

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