Games

8 pages, 247 KiB

Open AccessCommunication

A Controlled Discrete-Time Queueing System as a Model for the Orders of Two Competing Companies

by Mario Lefebvre

Games 2024, 15(3), 19; https://doi.org/10.3390/g15030019 (registering DOI) - 29 May 2024

We consider two companies that are competing for orders. Let

X_{1} (n)

denote the number of orders processed by the first company at time n, and let

τ (k)

be the first time that [...] Read more.

We consider two companies that are competing for orders. Let

X_{1} (n)

denote the number of orders processed by the first company at time n, and let

τ (k)

be the first time that

X_{1} (n) < j

or

X_{1} (n) = r

, given that

X_{1} (0) = k

. We assume that

{X_{1} (n), n = 0, 1, \dots}

is a controlled discrete-time queueing system. Each company is using some control to increase its share of orders. The aim of the first company is to maximize the expected value of

τ (k)

, while its competitor tries to minimize this expected value. The optimal solution is obtained by making use of dynamic programming. Particular problems are solved explicitly. Full article

(This article belongs to the Section Applied Game Theory)

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25 pages, 957 KiB

Open AccessArticle

Information Transmission and Countervailing Biases in Organizations

by Saori Chiba

Games 2024, 15(3), 18; https://doi.org/10.3390/g15030018 - 22 May 2024

Viewed by 288

Abstract

A decision maker (DM) must choose between two projects or decide on no project. The expected benefits of these projects are correlated. The DM seeks advice from an agent with private information about the projects’ benefits. However, the agent’s divergent preferences for projects [...] Read more.

A decision maker (DM) must choose between two projects or decide on no project. The expected benefits of these projects are correlated. The DM seeks advice from an agent with private information about the projects’ benefits. However, the agent’s divergent preferences for projects and lack of consideration for the DM’s implementation costs may introduce two types of biases: project bias, favoring the agent’s project, or pandering bias, favoring the project preferred by the DM. Our findings reveal that project correlation leads to these biases countervailing each other, facilitating the transmission of information. The agent typically recommends a project based on private information to dissuade the DM from choosing no project, as this would be detrimental to the agent. Additionally, we explore optimal delegation within organizations. In contrast to the prevailing literature advocating for delegation to biased agents for enhanced information elicitation, our study suggests limited benefits in the context of project correlation. Full article

14 pages, 299 KiB

Open AccessArticle

Invariant Equilibrium in Discontinuous Bayesian Games

by Blake A. Allison and Jason J. Lepore

Games 2024, 15(3), 17; https://doi.org/10.3390/g15030017 - 20 May 2024

Viewed by 289

Abstract

We provide sufficient conditions on the primitives of a class of discontinuous Bayesian games such that all games in the class share equilibria. If a Bayesian game in the class also satisfies a weak efficiency condition, then we show its normal form is [...] Read more.

We provide sufficient conditions on the primitives of a class of discontinuous Bayesian games such that all games in the class share equilibria. If a Bayesian game in the class also satisfies a weak efficiency condition, then we show its normal form is better-reply secure. The invariance property then provides an existence result for all Bayesian games in the class. Results are shown for both pure strategy and behavioral strategy equilibrium. We illustrate the application of the results with an example of a class of contests with bid caps. Full article

(This article belongs to the Section Non-Cooperative Game Theory)

15 pages, 2987 KiB

Open AccessArticle

Evolution of “Pay-It-Forward” in the Presence of the Temptation to Free-Ride

by Satoshi Uchida, Tatsuya Sasaki, Hitoshi Yamamoto and Isamu Okada

Games 2024, 15(3), 16; https://doi.org/10.3390/g15030016 - 25 Apr 2024

Viewed by 1327

Abstract

“Paying it forward” is a behavior in which people help someone else because they were helped in the past. Although experimental evidence exists that indicates that real human beings often “pay-it-forward” even in the face of free-rider risks, the theoretical basis for the [...] Read more.

“Paying it forward” is a behavior in which people help someone else because they were helped in the past. Although experimental evidence exists that indicates that real human beings often “pay-it-forward” even in the face of free-rider risks, the theoretical basis for the evolution of this behavior remains unclear. In this paper, we propose a game-theoretical model that explains how pay-it-forward behavior can evolve despite the temptation to free-ride. By assuming that human beings exhibit cognitive distortions, as predicted by prospect theory, and that free-riding is punished with a tiny probability, we demonstrate that pay-it-forward, alongside unconditional altruistic behavior, can evolve and effectively deter free-riding behavior. Full article

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Games, Volume 15, Issue 3 (June 2024) – 4 articles

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