Nonlinear Decision Weights or Skewness Preference? A Model Competition

Spiliopoulos, Leonidas; Hertwig, Ralph

doi:10.2139/ssrn.2642829

Cited by 5 publications

(5 citation statements)

References 113 publications

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“…Estimating models per individual-whilst capturing individual heterogeneity-may not be the best line of attack due to the large number of free parameters and susceptibility to overfitting. Instead, we propose hierarchical latent-class models that capture both types of between-subjects heterogeneity with a reduction in free parameters (Conte and Hey 2013;Lee and Webb 2005;Scheibehenne et al 2013;Spiliopoulos 2012;Spiliopoulos and Hertwig 2015). The latent classes capture model heterogeneity, whereas the hierarchical structure models parameter heterogeneity.…”

Section: Hierarchical Latent-class Modelingmentioning

confidence: 99%

The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcomebased models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments. Keywords Response time Á Time constraints Á Experimental economics Á Procedural rationality Á Games Á Strategic decision making BCD refers to Benefits, Challenges, and Desiderata.

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Section: Hierarchical Latent-class Modelingmentioning

confidence: 99%

The BCD of Response Time Analysis in Experimental Economics

Spiliopoulos

Ortmann²

2014

SSRN Journal

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“…For example, given that skewness valuations in our experiments highly depended on estimation bias, we would expect that skewness preferences would be less pronounced in choices from description where estimation errors presumably are smaller. In line with this reasoning, the effect of skewness on preferences in description-based choices is indeed mixed (Åstebro et al, 2015;Lichtenstein, 1965;Spiliopoulos & Hertwig, 2015;Taleb, 2004). Consequently, when modeling economic behavior, researchers should consider both basic cognitive and genuine preferential components.…”

Section: Differences In the Presentation Format And Estimation Biasesmentioning

confidence: 81%

“…small outcomes occur with low probability and most samples are above the mean). A preference for right-skewed distributions is one way to explain buying lottery tickets and insurance at the same time (Golec & Tamarkin, 1998;Spiliopoulos & Hertwig, 2015). In line with this reasoning, the mean-variance model (Markowitz, 1952) was extended for skewness preferences with an additional parameter (Kraus & Litzenberger, 1976).…”

Section: Economic Preferencesmentioning

confidence: 99%

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How Basic Cognition Influences Experience-Based Economic Valuation

Olschewski¹,

Newell²,

Scheibehenne³

2019

Preprint

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The perception and integration of sequential numerical information is a common cognitive task. It is a prerequisite for experience-based economic choices, but it is usually not part of economic decision theory. To better understand the process of symbolic number integration and its influence on economic behavior, we performed three experimental studies that examined mean estimates and economic valuations of continuous number distributions. The results indicate that participants valued random number distributions below their respective arithmetic means and valued distributions as lower when their variance increased, indicating risk aversion. A similar though less pronounced pattern also occurred in the matched mean estimation task where accuracy was incentivized and preferences played no role. These patterns suggest that seemingly risk-averse preferences are partly due to cognitive biases when perceiving and estimating numbers. In addition, participants' apparent economic preference for right-skewed outcome distributions could be attributed mainly to estimation biases. We discuss the extent to which the results can be explained based on a compressed mental number line and different sample weighting models. Finally, a new model that can account for the qualitative data pattern and has stronger overweighting of lower than higher numbers as its core feature is developed. Together, our results indicate that basic cognitive processes in perceiving and integrating number sequences play a key role in understanding experience-based economic behavior.

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“…In economics, finite mixture models have predominantly been estimated using maximum-likelihood techniques (Bruhin, Fehr-Duda, & Epper, 2010; Conte, Hey, & Moffatt, 2011; Costa-Gomes et al, 2001; El-Gamal & Grether, 1995; Harrison & Rutström, 2009; Spiliopoulos, 2012). Notable exceptions that use Bayesian estimation include Houser, Keane, and McCabe (2004), Shachat and Wei (2012), Shachat, Swarthout, and Wei (2015), and Spiliopoulos and Hertwig (2015). Estimation of finite mixture models using maximum likelihood suffers from the possibility of settling in a local rather than global maximum.…”

Section: Strategy Classification Using a Bayesian Latent Class Modelmentioning

confidence: 99%

Complexity, attention, and choice in games under time constraints: A process analysis.

Spiliopoulos¹,

Ortmann²,

Zhang³

2018

Journal of Experimental Psychology: Learning, Memory, and Cogni

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We test empirically the strategic counterpart of the Adaptive Decision Maker hypothesis (Payne, Bettman, & Johnson, 1993), which states that decision makers adapt their attention and decision rules to time pressure in predictable ways. For 29 normal form games, we test whether players adapt to tightening time constraints by reducing their information search and shifting to less computationally demanding heuristics, ultimately leading to systematic changes in choices. We specify process models of each decision rule, thereby allowing us to rank the rules by complexity on the basis of the number of elementary information processing units required to execute each rule. The frequency of decision rule use conditional on time constraints is estimated using Bayesian latent class modeling. Under time pressure, the subject pool displayed a systematic shift toward heuristics of less complexity. We observed an increase in nonstrategic decision rules (particularly Level 1) and rules that choose the social optimum at the expense of more complex rules including the Nash equilibrium. Also, subjects are more likely to not acquire information about others' payoffs, thus effectively transforming the games into nonstrategic situations. The subjects' adaptation to time pressure was efficient in the sense that it did not lead to significant payoff losses. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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Nonlinear Decision Weights or Skewness Preference? A Model Competition

Cited by 5 publications

References 113 publications

The BCD of Response Time Analysis in Experimental Economics

The BCD of Response Time Analysis in Experimental Economics

How Basic Cognition Influences Experience-Based Economic Valuation

Complexity, attention, and choice in games under time constraints: A process analysis.

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