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This paper explores the role of neural networks for decision making in dynamic environments which are characterized by risks and uncertainties, and also provides experimental evidence from a simulated data. Theoretical support is derived from theories of affective balance, and self‐organized criticality. The simulation is conducted for a two‐person‐constant sum game. The findings of the experiment are helpful in extending to managerial decision making which involves varying degrees of uncertainties. Such decisions are affected by forces both internal and external to the company, and making judgments in such a fuzzy future is highly probabilistic. It is suggested, therefore that neural networks are better able to capture the interactive dynamics of variables operating in a managerial decision environment. In sum, the findings indicate that decisions in general and business decisions in particular can greatly benefit from the parallel computational capabilities of neural networks.

The purpose of this paper is to illustrate how game theoretic solution concepts inform what classes of problems will be amenable to artificial intelligence and machine learning (AI/ML), and how to evolve the interaction between human and artificial intelligence.

The approach addresses the development of operational gaming to support planning and decision making. It then provides a succinct summary of game theory for those designing and using games, with an emphasis on information conditions and solution concepts. It addresses how experimentation demonstrates where human decisions differ from game theoretic solution concepts and how games have been used to develop AI/ML. It concludes by suggesting what classes of problems will be amenable to AI/ML, and which will not. It goes on to propose a method for evolving human/artificial intelligence.

Game theoretic solution concepts inform classes of problems where AI/ML 'solutions' will be suspect. The complexity of the subject requires a campaign of learning.

Though games have been essential to the development of AI/ML, practitioners have yet to employ game theory to understand its limitations.

This paper suggests to combine different kind of “other-regarding” preferences as an approach to fair behavior which is observed in controlled experiments. We assert that participants in two-person experiments have a good will capital which may be described by altruistic preferences. These preferences guide a large fraction of participants when they have to make distributional choices in one-stage games. We further show that in games with more than one stage the previous action of the other person may cause reciprocal feelings in addition to the altruistic preferences. A friendly (unfriedly) act of the other person may increase (decrease) the good will capital of the participants. Upon these findings, we conclude that a combination of altruism and reciprocity is able to describe the variety of behavior in several experiments despite their differing strategic context.

The basic ideas of Cournot and those who came after him are related to the recent work of Nash and his notion of an “equilibrium point.” It is shown that the Nash equilibrium point incorporates the main contribution of Cournot to the solution of the duopoly problem and that the major criticism that may be made against the Cournot equilibrium may also be made against the Nash equilibrium. It is then indicated to what use this weakness might be put in the study of bargaining.

One important assumption in the conventional cooperative game theory is that payoffs are assumed to be deterministic. In terms of the players’ cognitive ability of the realistic world, this is a very strict assumption. The classical game theory can find no way out when a particular game circumstance involves uncertainty, such as limited knowledge, small sample, and inadequate information, the payoff values of the game can only be described with interval grey numbers. The paper aims to discuss these issues.

In this study the concept of N-person grey games is proposed in which payoffs are represented with interval grey numbers opposed to the classical game theory. A straight forward solution methodology is submitted compatible to grey numbers. Then, a currency war between anonymous countries is handled and modeled as an N-Person grey game. A generic currency war scenario is developed to follow the proposed solution procedure thoroughly.

Based on the outcomes of this work, the authors can say that N-person grey game is an expansion of the classical N-person game under uncertain grey information and can be applied in more complex and uncertain environments, such as those seen in complicated currency warfare.

This study combines the grey system theory with the classic N-person game theory and sets up the N-person grey game with grey payoff functions. Based on the grey number operating methods, the grey linear programming algorithm is established to calculate and distribute benefits to the players. In this respect this study has the feature of being the pioneer in the N-person grey game area.

Rational choice theory has numerous implications for the analysis of organizational governance structures. This chapter reviews some of these applications. The main emphasis is on relational contracting. It will be argued that repeated games theory, that is, a variant of rational choice that deals with rational agents who repeatedly interact, can explain the outcomes of relational contracting. There is some controversy about the merits of rational choice explanations. Can they deal with inefficient structures and their (alleged) stability, with path dependence and mimetic processes? Many of these issues have been addressed by new sociological institutionalists. It is argued that rational choice analysis is in fact consistent with many of these observations. There is, in other words, some convergence between rational choice and institutionalist approaches.

Two‐person zero‐sum game theory has long been a popular topic of research in business and economics. The purpose of this paper is to discuss how to convert a two‐person zero‐sum game into a linear programming problem and to present a computer simulation model for solving large bilateral zero‐sum games problems.

A new method to solve zero‐sum two‐person games with imprecise values in their matrices of pay‐offs is suggested. The natural lack of precision generated by the use of fuzzy numbers in a fuzzy game requires the use of subjective criteria by the players in the resolution model. We apply a ranking function, the Average Value, which allows the decision makers to take into account their subjectivity. The use of this function raises again the solution of the fuzzy game when two criteria, one for each player, are used.

The purpose of this paper is to investigate knowledge sharing (KS) to characterize its behavior in companies based on the concepts of theory of reasoned action and game theory (GT).

The paper proposes two models: Model A: KS process is constructed by capturing personal psychological feelings; and Model B: KS process is not only constructed by personal psychological feelings but also takes into consideration other people's decisions. By comparing the two knowledge‐sharing models' predictability performance, the authors are able to characterize the process of KS.

The results show that different companies require different models. On average, Model B with game concept has lower predictive accuracy than the Model A without game concept.

The paper provides practical implications for manager to frame effective KS policies and also suggests future studies to improve measurement.

The values of this paper are: provide a methodology to determine whether individuals analyze decisions of others in making KS decisions; test the predictive ability of GT analysis in a KS modeling, and build a single‐instance two‐person game to characterize individuals' tacit KS behavior.