Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models

Fairness in competitive games such as the Ultimatum Game is often defined theoretically. According to some of the literature, in which fairness is determined only based on resource allocation, a proposal splitting resources evenly (i.e., 5:5) is generally assumed as fair, and minimal deviation (i.e....

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Main Authors: Marco Biella, Max Hennig, Laura Oswald
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Games
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Online Access:https://www.mdpi.com/2073-4336/16/1/2
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author Marco Biella
Max Hennig
Laura Oswald
author_facet Marco Biella
Max Hennig
Laura Oswald
author_sort Marco Biella
collection DOAJ
description Fairness in competitive games such as the Ultimatum Game is often defined theoretically. According to some of the literature, in which fairness is determined only based on resource allocation, a proposal splitting resources evenly (i.e., 5:5) is generally assumed as fair, and minimal deviation (i.e., 4:6) is considered enough to classify the proposal as unfair. Relying on multinomial processing tree models (MPTs), we investigated where the boundaries of fairness are located in the eye of responders, and pit fairness against relative and absolute gain maximization principles. The MPT models we developed and validated allowed us to separate three individual processes driving responses in the standard and Third-Party Ultimatum Game. The results show that, from the responder’s perspective, the boundaries of fairness encompass proposals splitting resources in a perfectly even way and include uneven proposals with minimal deviance (4:6 and 6:4). Moreover, the results show that, in the context of Third-Party Ultimatum Games, the responder must not be indifferent between favoring the proposer and the receiver, demonstrating a boundary condition of the developed model. If the responder is perfectly indifferent, absolute and relative gain maximization are theoretically unidentifiable. This theoretical and practical constraint limits the scope of our theory, which does not apply in the case of a perfectly indifferent decision-maker.
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spelling doaj-art-8a9d8055164a464cba32c102132bc5bd2025-08-20T03:11:21ZengMDPI AGGames2073-43362025-01-01161210.3390/g16010002Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree ModelsMarco Biella0Max Hennig1Laura Oswald2Faculty of Business and Economics, University of Basel, 4052 Basel, SwitzerlandDepartment of Psychology, Julius-Maximilians-Universität Wuerzburg, 97070 Würzburg, GermanyDepartment of Psychology, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, GermanyFairness in competitive games such as the Ultimatum Game is often defined theoretically. According to some of the literature, in which fairness is determined only based on resource allocation, a proposal splitting resources evenly (i.e., 5:5) is generally assumed as fair, and minimal deviation (i.e., 4:6) is considered enough to classify the proposal as unfair. Relying on multinomial processing tree models (MPTs), we investigated where the boundaries of fairness are located in the eye of responders, and pit fairness against relative and absolute gain maximization principles. The MPT models we developed and validated allowed us to separate three individual processes driving responses in the standard and Third-Party Ultimatum Game. The results show that, from the responder’s perspective, the boundaries of fairness encompass proposals splitting resources in a perfectly even way and include uneven proposals with minimal deviance (4:6 and 6:4). Moreover, the results show that, in the context of Third-Party Ultimatum Games, the responder must not be indifferent between favoring the proposer and the receiver, demonstrating a boundary condition of the developed model. If the responder is perfectly indifferent, absolute and relative gain maximization are theoretically unidentifiable. This theoretical and practical constraint limits the scope of our theory, which does not apply in the case of a perfectly indifferent decision-maker.https://www.mdpi.com/2073-4336/16/1/2fairnesscompetitive gamesUltimatum Gamemultinomial processing treerelative gain maximizationutility theory
spellingShingle Marco Biella
Max Hennig
Laura Oswald
Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
Games
fairness
competitive games
Ultimatum Game
multinomial processing tree
relative gain maximization
utility theory
title Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
title_full Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
title_fullStr Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
title_full_unstemmed Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
title_short Investigating the Social Boundaries of Fairness by Modeling Ultimatum Game Responders’ Decisions with Multinomial Processing Tree Models
title_sort investigating the social boundaries of fairness by modeling ultimatum game responders decisions with multinomial processing tree models
topic fairness
competitive games
Ultimatum Game
multinomial processing tree
relative gain maximization
utility theory
url https://www.mdpi.com/2073-4336/16/1/2
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AT maxhennig investigatingthesocialboundariesoffairnessbymodelingultimatumgamerespondersdecisionswithmultinomialprocessingtreemodels
AT lauraoswald investigatingthesocialboundariesoffairnessbymodelingultimatumgamerespondersdecisionswithmultinomialprocessingtreemodels