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Games
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The Empirics of Behaviour under Risk and Ambiguity
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The Empirics of Behaviour under Risk and Ambiguity

A special issue of Games (ISSN 2073-4336).

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 21331

Special Issue Editors

Prof. Dr. Anna Conte

E-Mail Website
Guest Editor
Department of Management, Sapienza University of Rome, 00185 Rome, Italy
Interests: econometric modelling of experimental data (experimetrics); structural model estimation; monte Carlo simulation techniques; finite and continuous mixture models; estimation of limited dependent variable models; panel data; simultaneous equation systems; survey methodology; survey data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. John Hey

E-Mail Website
Guest Editor
Emeritus Professor of Economics and Statistics and Director of the Centre for Experimental Economics (EXEC), Department of Economics and Related Studies, University of York, UK
Interests: individual decision-making under risk and ambiguity, both static and dynamic, using experimental methods

Special Issue Information

Dear Colleagues, 

The last decades have witnessed several researchers challenging themselves with the modelling and testing of competing theories of choice under risk and ambiguity. The aim of this Special Issue is to follow in these footsteps, welcoming submissions mainly, but not exclusively, on experimental and econometric studies on this topic. The context can be any, including individual choice, group choice and strategic choice. We encourage the submission of studies which focus on the heterogeneity of behaviour under risk and/or ambiguity and its identification. Literature reviews or appraisals of existing literature are also encouraged. 

Dr. Anna Conte
Prof. John Hey
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Games is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Experimental elicitation of behaviour under risk and ambiguity
  • Estimation of behaviour under risk and ambiguity
  • Expected Utility theory
  • non-Expected Utility theories
  • Heterogeneity
  • Error stories

Published Papers (4 papers)

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Research

27 pages, 904 KiB  
Article
Measuring and Comparing Two Kinds of Rationalizable Opportunity Cost in Mixture Models
by James R. Bland
Games 2020, 11(1), 1; https://doi.org/10.3390/g11010001 - 19 Dec 2019
Viewed by 4688
Abstract
In experiments of decision-making under risk, structural mixture models allow us to take a menu of theories about decision-making to the data, estimating the fraction of people who behave according to each model. While studies using mixture models typically focus only on how [...] Read more.
In experiments of decision-making under risk, structural mixture models allow us to take a menu of theories about decision-making to the data, estimating the fraction of people who behave according to each model. While studies using mixture models typically focus only on how prevalent each of these theories is in people’s decisions, they can also be used to assess how much better this menu of theories organizes people’s utility than does just one theory on its own. I develop a framework for calculating and comparing two kinds of rationalizable opportunity cost from these mixture models. The first is associated with model mis-classification: How much worse off is a decision-maker if they are forced to behave according to model A, when they are in fact a model B type? The second relates to the mixture model’s probabilistic choice rule: How much worse off are subjects because they make probabilistic, rather than deterministic, choices? If the first quantity dominates, then one can conclude that model a constitutes an economically significant departure from model B in the utility domain. On the other hand, if the second cost dominates, then models a and B have similar utility implications. I demonstrate this framework on data from an existing experiment on decision-making under risk. Full article
(This article belongs to the Special Issue The Empirics of Behaviour under Risk and Ambiguity)
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12 pages, 1074 KiB  
Article
A Bayesian Method for Characterizing Population Heterogeneity
by Dale O. Stahl
Games 2019, 10(4), 40; https://doi.org/10.3390/g10040040 - 09 Oct 2019
Viewed by 4074
Abstract
A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the [...] Read more.
A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject population is not behaviorally distinguishable from the ordinary expected utility model. Full article
(This article belongs to the Special Issue The Empirics of Behaviour under Risk and Ambiguity)
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28 pages, 498 KiB  
Article
Emotion and Knowledge in Decision Making under Uncertainty
by Anna Maffioletti and Michele Santoni
Games 2019, 10(4), 36; https://doi.org/10.3390/g10040036 - 27 Sep 2019
Cited by 2 | Viewed by 5085
Abstract
This paper presents four incentivised experiments analysing jointly the separate role of immediate integral emotions and knowledge in individual decision making under ambiguity. Reactions to a natural source of uncertainty (i.e., forthcoming real-world election results) were measured using both computed decision weights derived [...] Read more.
This paper presents four incentivised experiments analysing jointly the separate role of immediate integral emotions and knowledge in individual decision making under ambiguity. Reactions to a natural source of uncertainty (i.e., forthcoming real-world election results) were measured using both computed decision weights derived from individual choices and judgmental probabilities determined from the subjects’ estimated likelihood of election outcomes. This study used self-reports to measure emotions aroused by the prospective election victory of a party/coalition of parties, and both self-assessed and actual competence to measure knowledge of politics. This paper found evidence of both preference for ambiguity in the gain domain and of likelihood insensitivity, namely the tendency to overweight unlikely events and to underweight likely events. This paper also shows that a superior knowledge of politics was associated with a preference for ambiguity (i.e., the elevation of the decision weighting function for gains). Both stronger positive emotions and superior knowledge generally have asymmetric effects on likelihood insensitivity (i.e., the curvature of the decision weighting function), each being associated separately with higher overweighting of unlikely election outcomes. Full article
(This article belongs to the Special Issue The Empirics of Behaviour under Risk and Ambiguity)
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22 pages, 600 KiB  
Article
Measuring and Disentangling Ambiguity and Confidence in the Lab
by Daniela Di Cagno and Daniela Grieco
Games 2019, 10(1), 9; https://doi.org/10.3390/g10010009 - 18 Feb 2019
Cited by 3 | Viewed by 6742
Abstract
In this paper we present a novel experimental procedure aimed at better understanding the interaction between confidence and ambiguity attitudes in individual decision making. Different ambiguity settings not only can be determined by the lack of information in possible scenarios completely “external” to [...] Read more.
In this paper we present a novel experimental procedure aimed at better understanding the interaction between confidence and ambiguity attitudes in individual decision making. Different ambiguity settings not only can be determined by the lack of information in possible scenarios completely “external” to the decision-maker, but can also be a consequence of the decision maker’s ignorance about her own characteristics or performance and, thus, deals with confidence. We design a multistage experiment where subjects face different sources of ambiguity and where we are able to control for self-assessed levels of competence. By means of a Principal Component Analysis, we obtain a set of measures of “internal” and “external” ambiguity aversion. Our regressions show that the two measures are significantly correlated at the subject level, that the subjects’ “internal” ambiguity aversion increases in performance in the high-competence task and that “external” ambiguity aversion moderately increases in earnings. Self-selection does not play any role. Full article
(This article belongs to the Special Issue The Empirics of Behaviour under Risk and Ambiguity)
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