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Selected Topics in Behavioral Economics: Competitiveness, Early Childhood Development, Communicating Externalities, Empathy and Trust

Paper Session

Friday, Jan. 6, 2023 2:30 PM - 4:30 PM (CST)

New Orleans Marriott, Balcony I
Hosted By: Society for the Advancement of Behavioral Economics
  • Chair: Mark Pingle, University of Nevada-Reno

Non-clairvoyant Dynamic Mechanism Design: Experimental Evidence

Shan Gui
,
George Mason University
Daniel Houser
,
George Mason University

Abstract

Dynamic mechanisms are powerful approaches for optimizing the revenue and efficiency of repeated auctions. Implementing these approaches is made complicated, however, by a number of conditions that are difficult to satisfy in practice. These include that the auction designer must be clairvoyant, in the sense that they must have reliable forecasts of participants’ valuation distributions in all future periods. Recently, Mirrokni et al. (2020) introduced a non-clairvoyant dynamic mechanism and showed it is optimal within the class of dynamic mechanisms that do not rely on strong assumptions regarding knowledge about the future. We showed, however, however, that an optimal static mechanism (a Myerson auction) can under certain conditions outperform their dynamic mechanism. Here, we report data from an experiment designed to test the performance of their mechanism. Our results support the theory: the optimal non-clairvoyant dynamic mechanism outperforms the repeated optimal static mechanism when it is predicted to do so, and underperforms when theory predicts it should. Our results point to the practical importance of non-clairvoyant mechanisms as implementable approaches to dynamic auction design.

Are You Listening? The Impact of Parental Communications on Early Childhood Development

Majid Ahmadi
,
University of Chicago
John List
,
University of Chicago
Arnoldo Muller-Molina
,
University of Chicago
Julie Pernaudet
,
University of Chicago
Dana Suskind
,
University of Chicago

Abstract

This paper develops a Machine Learning (ML) based algorithm that identifies parental behaviors that lead to higher cognitive and non-cognitive development in children. The tone algorithm developed here extracts acoustic features from recorded audios of a home visiting experiment that has been implemented on 90 parent-child dyads followed pre- and post-intervention for a year. These features cover a wide range of features in time and frequency domain for each one second frame for all audio files in the dataset, and then use a neural network model to identify the speaker (mother, father, child) in each one second frame. In addition, developmental outcomes for children (including vocabulary, math, and socio-emotional scores) have been measured post-intervention. Statistical analysis and decision tree models, such as Random Forest, for children in the treatment and control groups will be applied to identify positive (reinforcing) and negative (weakening) behaviors. Since the number of extracted features from each audio is large, the algorithm uses unsupervised learning methods such as autoencoder for dimensionality reduction. Finally, the identified set of parental interactions are compared with several speaking styles (such as “parentese”) that are well-studied by researchers to promote higher brain development.

The impact of narratives on opinions: Evidence regarding negative externalities

John Ifcher
,
Santa Clara University
Sandra Goff
,
Bates College

Abstract

We use factorial vignette design to examine whether people believe it is fair to impose external costs on bystanders when engaging in market activity, and whether fairness attitudes are influenced by the framing and narrative used to communicate about the external costs. We present participants with five vignettes in which the framing and narrative used to describe the external cost vary randomly. Participants are asked to rate both the fairness of the externality-producing behavior and a government intervention meant to address the external costs. We find that participants generally perceive negative externalities as not fair and government actions meant to ameliorate the externality as fair.

Empathy and Trust

Mark Pingle
,
University of Nevada-Reno
Federico Guerrero
,
University of Nevada-Reno
Ben Albrecht
,
University of Nevada-Reno

Abstract

The investment game of Berg et al (1995) is more commonly referred to as the trust game because a first mover transferring value to the second mover is trusting that something will over-ride the material self-interest of the second mover. Berg et al identified positive reciprocity as the factor most likely motivating the observed trustworthiness of second movers in their game. However, there are other possible explanations, including altruism, a desire to see value created, inequality aversion, and more. Our “empathy game” as a simplified version of the trust game, which strips away positive reciprocity and most factors that might motivate trustworthiness, but leaves empathy as a possible motivator. The results indicate empathy is a weak motivator, but it is sufficient to over-ride self-interest. To the extent that empathy is a useful quality, those who have interest can use the empathy game to measure empathy.
JEL Classifications
  • D9 - Micro-Based Behavioral Economics
  • C9 - Design of Experiments