Maximum Payoff Research Articles

The Development Dilemma and Path Choice of Innovation and Entrepreneurship Education Based on Game Theory

Innovation and entrepreneurship education (IEE) is a powerful driving force to promote the transformation and development of college education and an effective way to broaden the employment channels. However, IEE has been used to train students in classrooms by college managers who do not systematically think of practical training for innovation and entrepreneurship talents. There are still many difficulties in the process of development of IEE that managers cannot ignore. The relevant models that could deeply analyze the connotation of IEE were established through the game theory that explained the game balance among government, universities, and students in the IEE process. A game theory model explained the external game behavior of universities and students and the internal game behavior between students. Another model analyzed the strategies of players pursuing maximum payoffs from the perspective of both sides of the game and the influences of these strategies on the players. The problems in the process of IEE development are concluded including imperfect management system, unscientific curriculum design, nonideal talent training and unprofessional teachers. Based on it, the development path of IEE is explored through the game concept. A scientific and leading system of management and evaluation is needed to put forward by strengthening the top-level design at the national level, and a win-win game situation between colleges and students will be achieved by the optimized education system.

A Robust Approach for Privacy Data Protection: IoT Security Assurance Using Generative Adversarial Imitation Learning

With the increasing importance of data security, privacy protection has gradually risen to a strategic position, especially IoT data privacy protection. The concern for data security has become a national strategy. The discovery of potential risks of privacy data is of great significance, such as the risk of data privacy leakage, data security vulnerabilities, etc. In this article, starting from the privacy data protection mechanism in the Industrial Internet of Things (IIoT) scenario, we proposed a method based on generative adversarial imitation learning (GAIL) to discover the privacy data security risks in IIoT by training privacy protection agents using a large amount of expert data on privacy protection. Finally, our proposed method is validated by relevant simulation experiments, and the results show that our proposed method has wide generalizability and reliability to obtain the maximum payoff of the agents and thus, reduce the risk of data security leakage.

Dynamics of a Stratified Population of Optimum Seeking Agents on a Network—Part II: Steady-State Analysis

In this second part of our work, we study the steady state of the population and the social utility for a general class of dynamics that converge to the set of Nash equilibria and follow a certain positive correlation property. This class of dynamics includes the three dynamics introduced in the first part. We provide sufficient conditions on the network based on a maximum payoff density parameter of each node under which there exists a unique Nash equilibrium. We then utilize the positive correlation properties of the dynamics to reduce the flow graph in order to provide an upper bound on the steady-state social utility. Finally, we extend the idea behind the sufficient condition for the existence of a unique Nash equilibrium to partition the graph appropriately in order to provide a lower bound on the steady-state social utility. We also illustrate interesting cases as well as our results using simulations.

Evolutionary instability of selfish learning in repeated games.

Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.

Direct reciprocity between individuals that use different strategy spaces.

In repeated interactions, players can use strategies that respond to the outcome of previous rounds. Much of the existing literature on direct reciprocity assumes that all competing individuals use the same strategy space. Here, we study both learning and evolutionary dynamics of players that differ in the strategy space they explore. We focus on the infinitely repeated donation game and compare three natural strategy spaces: memory-1 strategies, which consider the last moves of both players, reactive strategies, which respond to the last move of the co-player, and unconditional strategies. These three strategy spaces differ in the memory capacity that is needed. We compute the long term average payoff that is achieved in a pairwise learning process. We find that smaller strategy spaces can dominate larger ones. For weak selection, unconditional players dominate both reactive and memory-1 players. For intermediate selection, reactive players dominate memory-1 players. Only for strong selection and low cost-to-benefit ratio, memory-1 players dominate the others. We observe that the supergame between strategy spaces can be a social dilemma: maximum payoff is achieved if both players explore a larger strategy space, but smaller strategy spaces dominate.

Generalized Assignment for Multi-Robot Systems via Distributed Branch-And-Price

In this article, we consider a network of agents that has to self-assign a set of tasks while respecting resource constraints. One possible formulation is the generalized assignment problem, where the goal is to find a maximum payoff while satisfying capability constraints. We propose a purely distributed branch-and-price algorithm to solve this problem in a cooperative fashion. Inspired by classical (centralized) branch-and-price schemes, in the proposed algorithm, each agent locally solves small linear programs, generates columns by solving simple knapsack problems, and communicates to its neighbors a fixed number of basic columns. We prove finite-time convergence of the algorithm to an optimal solution of the problem. Then, we apply the proposed scheme to a generalized assignment scenario, in which a team of robots has to serve a set of tasks. We implement the proposed algorithm in a Robot Operating System testbed and provide experiments for a team of heterogeneous robots solving the assignment problem.

Designing an Automatic Agent for Repeated Language–based Persuasion Games

AbstractPersuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized messages that do not consist of rich human language. In this paper we consider a repeated sender (expert) – receiver (decision maker) game, where the sender is fully informed about the state of the world and aims to persuade the receiver to accept a deal by sending one of several possible natural language reviews. We design an automatic expert that plays this repeated game, aiming to achieve the maximal payoff. Our expert is implemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep learning models that exploit behavioral and linguistic signals in order to predict the next action of the decision maker, and the future payoff of the expert given the state of the game and a candidate review. We demonstrate the superiority of our expert over strong baselines and its adaptability to different decision makers and potential proposed deals.1

Financing Disruptive Suppliers: Payment Advance, Timeline, and Discount Rate

This study considers a dyadic supply chain in which a large and creditworthy buyer procures a product from a capital‐constrained supplier subject to disruption risk. In facilitating the production, the buyer may offer direct financing to the supplier by way of advance payment (AP). Concurrently, the buyer may also set a tailored discount rate (TR) that applies to the AP and an extended payment timeline (PE) for the balance due. We analyze the value and interplay of these elements by comparing optimal contractual terms with different AP, PE, and TR potentials. In general, AP applies to more reliable suppliers, and the coverage could be broadened by the inclusion of PE and TR. Specifically, when the advance discount rate is regulated within a certain limit, the buyer should offer TR without PE to the most reliable suppliers, vs. the floor discount rate with PE to those posing higher risk. Although the buyer normally benefits from practicing PE, the supplier benefits from it only when the risk level is relatively high and suffers from it when the risk level is relatively low; these effects persist although they are weakened in the presence of TR. Overall, PE and TR focus on different risk spectrums and are strategic substitutes for each other. The buyer can thereby retain its maximum payoff by properly configuring PE or TR when the other is under strict regulation. These insights offer strategic guidance for buyers to engineer business cash flows with respect to the risk level of their suppliers and the external regulation environment.

AFSOS: An Auction Framework and Stackelberg Game Oriented Optimal Network’s Resource Selection Technique in Cognitive Radio Networks

The upcoming 5G and beyond wireless networking technologies are intended to coexist with different communication networks to solve the spectrum scarcity problem. The seamless roaming across integrated wireless networks and an optimal network’s resource allocation would be a doable challenge for designing futuristic networks. The cognitive radio (CR) networks, with the inclusion of game theory, can handle spectrum scarcity issues by permitting spectrum access to the secondary user (SU) in the absence of a primary user (PU). This work proposes an AFSOS (an Auction Framework and Stackelberg game oriented Optimal network’s resource Selection) technique to achieve a maximum payoff and acceptable interference power restraint in CR networks. The contest among candidate networks can be modeled as a Stackelberg game, and the contest between CR nodes and candidate networks can be modeled as an auction framework. As a schema for the realization and capabilities evaluations, the CR networks consisting of LTE cellular network interworking with Wi-Fi network for multiple access points (APs)/single vehicle (low traffic environment) is analyzed. This framework is further analyzed for single AP/multiple CR nodes (high traffic environment). The equilibrium for the Stackelberg game is achieved, and the proposed AFSOS technique is analyzed with numerical examples. The results show that the proposed AFSOS technique is more operative to obtain maximum payoff.

Time-Discrete Hedging of Down-and-Out Puts with Overnight Trading Gaps

Hedging down-and-out puts (and up-and-out calls), where the maximum payoff is reached just before a barrier is hit that would render the claim worthless afterwards, is challenging. All hedging methods potentially lead to large errors when the underlying is already close to the barrier and the hedge portfolio can only be adjusted in discrete time intervals. In this paper, we analyze this hedging situation, especially the case of overnight trading gaps. We show how a position in a short-term vanilla call option can be used for efficient hedging. Using a mean-variance hedging approach, we calculate optimal hedge ratios for both the underlying and call options as hedge instruments. We derive semi-analytical formulas for optimal hedge ratios in a Black–Scholes setting for continuous trading (as a benchmark) and in the case of trading gaps. For more complex models, we show in a numerical study that the semi-analytical formulas can be used as a sufficient approximation, even when stochastic volatility and jumps are present.

Quantum absentminded driver problem revisited

The aim of the paper is to study the problem of absentminded driver in the quantum domain. In the classical case, it is a well-known example of a decision problem with imperfect recall that exhibits lack of equivalence between mixed and behavioral strategies. The optimal payoff outcome is significantly lower than the maximum payoff appearing in the game. This raises the question whether a quantum approach to the problem can increase the strategic position of the decision maker. The results that we present in the paper clearly reveal the benefits from playing the absentminded problem with the aid of quantum objects. Through appropriately chosen initial quantum state, the unitary strategies enable the decision maker to obtain the maximum possible payoff. At the same time, our scheme comes down to the classical problem with a suitable restriction of unitary strategies.

Game theoretic approach for flood risk management considering a financial model

In process of flood risk management, there is usually a conflict between the governments, insurance companies and people as players in sharing financial responsibilities, because of how calculate insurance premium, compensation and governmental assistances. In order to this conflict resolution, in this paper a new methodology is proposed based on game theoretic approach by using Nash equilibrium method, that for the first time, the payoff functions of insurance companies and government have been associated to each other. In this method, according to maximization of player's payoffs, the best response has been found and for the first time, comprehensive financial model has been developed for sharing players' financial responsibilities in flood risk management plans. In order to increase the accuracy of flood insurance calculations, the parameters of deductible and franchise have been applied. The key findings indicate, success of flood risk management plans depend on purchasing flood insurance with full coverage and government financial assistances simultaneously. The developed flood insurance plan has most sensitivity to parameters of deductible and insurance rate. This model could be utilized as practical and academic model in flood risk management. The applicability and efficiency of the methodology are examined in Northeast of Tehran as capital of Iran.

The evolution of R&D collaboration in inter-organizational project networks: Effects of reference points for competitive preference

This study examines the evolution of R&D collaboration in inter-organizational project networks by simulating the play of an appropriate game model. We assume that players display reference-dependent competitive preferences, in that each player’s utility is the sum of a material payoff and a subjective utility that depends on the difference between her material payoff and those of her neighbors. Some players, called max-players, are aggressively competitive, comparing their payoffs to the maximum payoff of their neighbors; the others, called min-players, are prudent, comparing their payoffs to their neighbors’ minimum. In each period, a player decides whether to share a high level or a low level of knowledge with her neighbors. We find that the mean collaboration level across the network decreases as a parameter representing the ability to make use of a partner’s knowledge increases. When the majority of decision-makers are min-players, a higher competitive preference intensity induces greater average collaboration, but the level of collaboration decreases as preference heterogeneity increases. In contrast, a high intensity of competitive preference prevents the diffusion of collaboration when most decision-makers are max-players, and increased competitive preference heterogeneity leads to greater collaboration. Furthermore, increasing the fraction of max-players leads to a decrease in the total payoff of the entire network. Collaboration in an inter-organizational network is more robust when most decision-makers are max-players. Nonetheless, max-players and min-players are equally likely to collaborate when either type is in the majority.

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

In order to maximize the work efficiency of wireless mobile charger, a payoff-maximization-based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi-node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade-off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

Symmetric Subgame Perfect Equilibria in Resource Allocation

We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems. (Bhaskar 2000) proposed one way to achieve this in 2-agent, 1-resource allocation games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We show that for any convention, there exists a symmetric subgame perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as the ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.

Medium Communication and Testing of Cooperative or Competitive Tendencies using Prisoner's Dilemma Games

This study aims to test whether the media used can predict the individual's tendency to collaborate (cooperatively) or compete in the negotiation process, and whether the use of certain media affects the maximum joint payoff of the negotiation process. This research is expected to contribute theoretically to the development of concepts concerning media richness and electronic negotiations as well as prisoner's dilemma games. This study uses an experimental design to explain the tendencies of collaboration and competition by using Prisoner's Dilemma Games. The participants are students from management programs at two universities in Indonesia. There are some limitations in this article, namely, the research model still has limitations in explaining the behavior of negotiators in making decisions, especially on negotiations with computer media. In fact, the use of computer media in negotiations encounters a number of geographic constraints, ie distance and time differences which may greatly affect the outcome of negotiations under different conditions of the study. Keywords: medium, negotiation, cooperative, competitive, Prisoner’s dillema games, payoff, outcome DOI: 10.7176/EJBM/13-15-03 Publication date: August 31 st 2021

Overlapping generations games with growing payoffs

We model n-person overlapping generations games with growing payoffs, the situation of which is considered as an economic growth. We show that each player can obtain his maximum one-shot payoff in equilibria.

A generalized approach to solve perfect Bayesian Nash equilibrium for practical network attack and defense

To address the incomplete information dynamic network attack and defense game in practice, this paper proposes a generalized approach to solve for perfect Bayesian Nash equilibrium (BNE) for practical network attack and defense. To consider “role-shifting” in the practical network attack and defense environment, the proposed approach substitutes solving the Nash equilibrium (NE) problem with a payoff (reward) maximization problem via a profound combination between the subgame perfect NE of the complete information dynamic game and the BNE of the incomplete information static game. Furthermore, to evaluate the effectiveness of the proposed approach, a representative signaling game with specific values is examined from a theoretical perspective. Finally, a real penetration test case targeting a web server is implemented to substantiate the effectiveness of the proposed approach from a practical perspective with some visual verifications and crucial penetration codes, where the attacker successfully obtains the ROOT authority (the highest authority) of the target web server.

Knowledge management systems usage: application of diffusion of innovation theory

Purpose The purpose of this study is to investigate the influence of technological antecedents on the usage of decision makers for the implemented knowledge management system (KMS) amongst Jordanian banks. This study extends the investigation by assessing the influence of knowledge or information quality on KMS usage. This study aims to assess whether knowledge or information quality is significantly correlated to system compatibility, relative advantage and complexity (technological antecedents). Design/methodology/approach The study model was developed by using Rogers’ diffusion of innovation (DOI) theory, on which seven hypotheses were developed. To examine these research hypotheses, a self-administered questionnaire was carried out with 341 decision makers who are using the KMS to perform their job-related activities. Structural equation modelling analysis of moment structures software was used for data analysis. Findings The findings revealed that decision makers usage of the implemented KMS’s is affected significantly by relative advantages, system complexity and knowledge quality, but not system compatibility. Moreover, the findings showed that knowledge quality is significantly correlated with DOI technological antecedents. Practical implications Bank managements are now in a better position to understand what kind of resources and supports are needed to achieve the maximum pay-off from KMS usage within their banks. This study has proved that it is not sufficient for Jordanian banks to focus solely on the system quality; they must also take the quality of knowledge or information (system output) as a critical factor that can affect their investments in KMS’s. Originality/value This study is one of the limited conducted studies to investigate the importance of KMS usage and related antecedents in the Arab world; particularly, in the context of the Jordanian banking sector. The findings of this study have contributed to the Jordanian financial sector for its vital evaluation of the KMS actual usage behaviour. Findings can be used by the Jordanian ministry of finance to improve the understanding of the factors influencing KMS usage in the financial sector. This study has contributed to reducing the gap of DOI literature amongst developed and developing countries, particularly in the Jordanian context.

Bayesian persuasion with optimal learning

We study a model of Bayesian persuasion between a designer and a receiver with one substantial deviation from the standard setup—the designer offers once and for all a single statistical experiment from which the receiver can acquire costly i.i.d. signals over time. Taking a 2-state-2-action environment and employing a tractable continuous-time framework, we fully characterize the optimal persuasion policy. When the receiver features high skepticism, the optimal policy is to immediately reveal the truth, which is true for a large set of primitives. We construct the designer’s maximum payoff and find a discontinuous drop in it as compared with the standard model. Unlike in many standard persuasion models, the designer is not able to appropriate all the rents of information disclosure while the receiver often achieves the highest possible benefit from being able to repeatedly sample from the strategically offered information structure.