Maximum Payoff Research Articles

Experimental Evidence on Unique Integer Games

We examine motives in unique positive integer games in a laboratory setting. In lowest (highest) unique positive integer games, the participant with the lowest (highest) integer choice that is unique wins. In addition to the payoff maximization motive, we identify and characterize psychological motives, including winning probability maximization and social welfare maximization. We study games where different action profiles correspond to different motives and games where they do not. This permits us to empirically estimate individual heterogeneity corresponding to these behavioral types and arrive at characterizations of behavior in unique integer games.

Game-Theoretic Solutions for Data Offloading in Next Generation Networks

In recent years, global mobile data traffic has seen an unprecedented increase. This is due to worldwide usage of smart devices, availability of fast internet connections, and the popularity of social media. The Mobile Network Operators (MNOs) are, therefore, facing problems in handling this huge traffic flow. Each type of traffic, including real-time video, audio, and text has its own Quality of Services (QoS) requirements which, if not met, may cause a sufficient loss of profit. Offloading of these traffics can be made more efficient so that values of QoS parameters are enhanced. In this work, we propose an incentive-based game-theoretic frame work for downloading data. The download of each type of data will get an incentive determined by the two-stage Stackelberg game. We model the communication among single Mobile Base Station (MBS) and multiple Access Points (APs) in a crowded metropolitan environment. The leader offers an economic incentive based on the traffic type and followers respond to the incentive and offload traffic accordingly. The model optimizes strategies of both the MBS and APs in order to make the best use of their utilities. For the analysis, we have used a combination of analytical and experimental methods. The numerical outcome characterized a direct process of the best possible offloading ratio and legalized the efficiency of the proposed game. Optimal incentives and optimal offloading was the achievement of our proposed game-theoretic approach. We have implemented the model in MATLAB, and the experimental results show a maximum payoff was achieved and the proposed scheme achieved Nash Equilibria.

An improvement to generalized regret based decision making method considering unreasonable alternatives

Regret decision theory is a classic theory for decision problem. Recently, Yager proposed a generalized regret based decision-making method, which calculates the effective regret associated with an alternative by aggregating this alternative's all regrets across all the possible states of nature. The generalized regret based decision-making method that can be applied in many fields is understandable and effective. However, as Yager pointed, an issue limits the application of this method, that is, the generalized regret based decision-making method is lack of indifference to irrelevant alternatives. In this paper, we analyze the cause of this issue, that is, unreasonable alternatives may change other alternatives' regrets by changing the maximal payoff under the occurrence of a state of nature. Furthermore, a new method based on original model is proposed to reduce the impact of unreasonable alternatives according to a parameter called impact factor defined to measure an alternative's quality. Finally, several numerical examples are illustrated to show this new method's effectiveness.

Corporate governance roles of information quality and corporate takeovers

We examine the corporate governance roles of information quality and the takeover market with asymmetric information regarding the value of the target firm. Increasing information quality improves the takeover efficiency however, a highly efficient takeover market also discourages the manager from exerting effort. We find that perfect information quality is not optimal for either current shareholders’ expected payoff maximization or expected firm value maximization. Furthermore, current shareholders prefer a lower level of information quality than the level that maximizes expected firm value, because of a misalignment between current shareholders’ value and total firm value. We also analyze the impact of antitakeover laws, and find that the passage of antitakeover laws may induce current shareholders to choose a higher level of information quality and thus increase expected firm value.

Goal Achievement Failure Drives Corticospinal Modulation in Promotion and Prevention Contexts.

When making decisions, people are typically differently sensitive to gains and losses according to the motivational context in which the choice is performed. As hypothesized by Regulatory Focus Theory (RFT), indeed, goals are supposed to change in relation to the set of possible outcomes. In particular, in a promotion context, the goal is achieving the maximal gain, whereas in a prevention context it turns into avoiding the greatest loss. We explored the neurophysiological counterpart of this phenomenon, by applying Transcranial Magnetic Stimulation (TMS) and recording the motor evoked potentials (MEPs) in participants taking part in an economic game, in which they observed actions conveying different goal attainment levels, framed in different motivational contexts. More than the actual value of the economic exchange involved in the game, what affected motor cortex excitability was the goal attainment failure, corresponding to not achieving the maximal payoff in a promotion context and not avoiding the greatest snatch in a prevention context. Therefore, the results provide support for the key predictions of RFT, identifying a neural signature for the goal attainment failure.

Inequality signals in dorsolateral prefrontal cortex inform social preference models.

Humans typically display inequality aversion in social situations, which manifests itself as a preference for fairer distributions of resources. However, people differ in the degree to which they dislike being worse off [disadvantageous inequality (DI) aversion] or better off [advantageous inequality (AI) aversion] than others. Competing models explain such behavior by focusing on aversion to payoff differences, maximization of total payoff or reciprocity. Using functional near-infrared spectroscopy, we asked which of these theories could better explain dorsolateral prefrontal cortex (dlPFC) activity while participants accepted or punished fair vs unfair monetary transfers in an anonymous norm compliance task. We found that while all participants exhibited DI aversion, there were substantial differences in preferences for AI, which were strongly predicted by dlPFC activation. Model comparisons revealed that both punishment behavior and prefrontal activity were best explained by a model that allowed for AI seeking rather than imposing aversion. Moreover, enhancing this model by taking into account behavioral response times, as a proxy for choice difficulty, further improved model fits. Our data provide evidence that the dlPFC encodes subjective values of payoff inequality and that this representation is richer than envisaged by standard models of social preferences.

Quality-aware incentive mechanism based on payoff maximization for mobile crowdsensing

Recent years, we have witnessed the explosion of smart devices. These smart devices are more and more powerful with a set of built in sensor devices, such as GPS, accelerometer, gyroscope, camera, etc. The large scale and powerful smart devices make the mobile crowdsensing applications which leverage public crowd equipped with various mobile devices for large scale sensing tasks be possible. In this paper, we study a critical problem of payoff maximization in mobile crowdsensing system with incentive mechanism. Due to the influence of various factors (e.g. sensor quality, noise, etc.), the quality of the sensed data contributed by individual users varies significantly. Obtaining the high quality sensed data with less expense is the ideal of sensing platforms. Therefore, we take the quality of individuals which is determined by the sensing platforms into incentive mechanism design. We propose to maximize the social welfare of the whole system, due to that the private parameters of the mobile users are unknown to the sensing platforms. It is impossible to solve the problem in a central manner. Then a dual decomposition method is employed to divide the social welfare maximization problem into sensing platforms’ local optimization problems and mobile users’ local optimization problems. Finally, distributed algorithms based on an iterative gradient descent method are designed to achieve the close-to-optimal solution. Extensive simulations demonstrate the effectiveness of the proposed incentive mechanism.

Nice to you, nicer to me: Does self-serving generosity diminish the reciprocal response?

Reciprocity has been shown to be sensitive to perceived intentions, however, not much is known about the intensity of reciprocal responses to the precise nature of those intentions. For example, a person can strategically appear to be kind while being self-serving or can be selflessly (genuinely) kind. Do these two intentions elicit different reciprocal reactions? We propose a conjecture that self-serving but generous actions diminish the positively reciprocal response, compared to selfless generous actions. We classify actions that increase a recipient’s maximum payoff, but by less than the giver’s maximum payoff, as being self-serving generous actions, while classifying actions that increase a recipient’s maximum payoff by more than the giver’s as selfless generous actions. We hypothesize that selfless generous actions are considered more generous than self-serving generous actions, and that self-serving generous actions will therefore result in a diminished reciprocal response. We test this conjecture using two novel experimental designs. We find some evidence that subjects perceive self-serving generous actions as being less generous than selfless generous actions, but no empirical support for our conjecture on the diminished reciprocal response.

Multi-source fusion-based security detection method for heterogeneous networks

In this paper, multi-source fusion-based detection method for the security of heterogeneous network is investigated. Fusion-based detection method exploits multi-source profiles from the whole network to make decisions on whether intrusion incident happens. A game theoretic analysis method for the concerned detection strategy is presented, where the attacker and defender are thought to be rational humans and they always try their best to get their maximum payoffs. A nonzero-sum game model is established to formulate the confrontation between the defender and attacker by considering the detection threshold and attack resource allocation as their strategies. The optimal strategies are then solved by using Nash equilibrium theory. The local optimal attack allocation scheme is firstly presented for heterogeneous network, which shows that with limited resources the attacker should only launch attacks to more valuable nodes and more attack resources should be allocated to more valuable nodes also. Then a general conclusion about the existence of the Nash equilibrium is given, which indicates that the Nash equilibrium must exist despite the attacks types. After that, an iterative method is presented for the calculation of the Nash equilibrium by considering DDoS attack as an example. Numerical simulations are shown to validate our theoretical results.

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks.

Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

Deterministic mechanisms, the revelation principle, and ex-post constraints

This note establishes a revelation principle in terms of payoff for deterministic mechanisms under ex-post constraints: the maximal payoff implementable by a feasible deterministic mechanism can also be implemented by a feasible deterministic direct mechanism.

Robust games: theory and application to a Cournot duopoly model

In this paper, the robust game model proposed by Aghassi and Bertsimas (Math Program Ser B 107:231–273, 2006) for matrix games is extended to games with a broader class of payoff functions. This is a distribution-free model of incomplete information for finite games where players adopt a robust-optimization approach to contend with payoff uncertainty. They are called robust players and seek the maximum guaranteed payoff given the strategy of the others. Consistently with this decision criterion, a set of strategies is an equilibrium, robust-optimization equilibrium, if each player’s strategy is a best response to the other player’s strategies, under the worst-case scenarios. The aim of the paper is twofold. In the first part, we provide robust-optimization equilibrium’s existence result for a quite general class of games and we prove that it exists a suitable value $$\epsilon $$ such that robust-optimization equilibria are a subset of $$\epsilon $$ -Nash equilibria of the nominal version, i.e., without uncertainty, of the robust game. This provides a theoretical motivation for the robust approach, as it provides new insight and a rational agent motivation for $$\epsilon $$ -Nash equilibrium. In the last part, we propose an application of the theory to a classical Cournot duopoly model which shows significant differences between the robust game and its nominal version.

Game-theoretic perspective of Ping-Pong protocol

We analyse Ping-Pong protocol from the point of view of a game. The analysis helps us in understanding the different strategies of a sender and an eavesdropper to gain the maximum payoff in the game. The study presented here characterizes strategies that lead to different Nash equilibriums. We further demonstrate the condition for Pareto optimality depending on the parameters used in the game. Moreover, we also analysed LM05 protocol and compared it with PP protocol from the point of view of a generic two-way QKD game with or without entanglement. Our results provide a deeper understanding of general two-way QKD protocols in terms of the security and payoffs of different stakeholders in the protocol.

Generalized regret based decision making

We describe the basic regret decision-making model for decision problems in which the payoff for a given alternative is uncertain and depends on the value of a variable called the state of nature. An important attribute of this model is the maximum payoff for the occurrence of a given state of nature. We note that the regret is based on the difference between the payoff we receive, given our choice of alternative, under the occurrence of a state of nature and the best payoff we could have received for that state of nature. We note the effective regret associated with an alternative is an aggregation of an alternative’s regrets across all the possible states of nature. Our objective is to select the alternative with minimum effective regret. We look at this for various methods of aggregating an alternative’s individual regrets across the different states of nature and various types of information about the uncertainty associated with the states of nature. One issue limiting the use of regret decision-making is its lack of indifference to irrelevant alternatives and the related openness to strategic manipulation by introducing alternatives to solely effect the determination of the maximum payoff. We begin to look at methods to reduce this effect.

Dividing Attention Between Tasks: Testing Whether Explicit Payoff Functions Elicit Optimal Dual-Task Performance.

We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2–3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual‐task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.

Behavior in Group Contests: A Review of Experimental Research

Group contests are ubiquitous. Some examples include warfare between countries, competition between political parties, team-incentives within firms, group sports, and rent-seeking. In order to succeed, members of the same group have incentives to cooperate with each other by expending individual efforts. However, since effort is costly, each member also has an incentive to abstain from expending any effort and instead free-ride on the efforts of other members. Contest theory shows that the intensity of competition between groups and the amount of free-riding within groups depend on the group size, sharing rule, group impact function, contest success function, and heterogeneity of players. We review experimental studies testing these theoretical predictions. Almost all studies of behavior in group contests find significant over-expenditure of effort relative to the theory. We discuss potential explanations for such over-expenditure, including the utility of winning, bounded rationality, relative payoff maximization, parochial altruism, and social identity. Despite over-expenditure, most studies find support for the comparative statics predictions of the theory (with the exception of the “group size paradox”). Finally, studies show that there are effective mechanisms that can promote within-group cooperation and conflict resolution mechanisms that can de-escalate and potentially eliminate between-group conflict.

Measurement-Device-Independent Approach to Entanglement Measures.

Within the context of semiquantum nonlocal games, the trust can be removed from the measurement devices in an entanglement-detection procedure. Here, we show that a similar approach can be taken to quantify the amount of entanglement. To be specific, first, we show that in this context, a small subset of semiquantum nonlocal games is necessary and sufficient for entanglement detection in the local operations and classical communication paradigm. Second, we prove that the maximum payoff for these games is a universal measure of entanglement which is convex and continuous. Third, we show that for the quantification of negative-partial-transpose entanglement, this subset can be further reduced down to a single arbitrary element. Importantly, our measure is measurement device independent by construction and operationally accessible. Finally, our approach straightforwardly extends to quantify the entanglement within any partitioning of multipartite quantum states.

Ego-Utility and Endogenous Information Acquisition; An Experimental Study

This paper examines endogenous decisions to acquire useful information. My experimental design tries to test predictions of ego-utility theories and other relevant theories about the decision-making process of agents in the environment with costless signals. Only slightly more than half of the subjects acquired an optimal number of the signals for payoff maximization. The results suggest that for the subjects making sub-optimal decisions, aversion to cognitive dissonance is the prevalent channel. Contrary to this, I find much less support for the ego-utility theory and theory of information ignorance in my setting. The availability of information alone does not automatically lead to an improvement in decisions.

Evolution of cooperative imitators in social networks

Many evolutionary game models for network reciprocity are based on an imitation dynamics, yet how semirational imitators prevail has seldom been explained. Here we use a model to investigate the coevolutionary dynamics of cooperation and partnership adjustment in a polygenic population of semirational imitators and rational payoff maximizers. A rational individual chooses a strategy best responding to its neighbors when updating strategy and switches to a new partner who can bring it the maximal payoff from all candidates when adjusting the partnership. In contrast, a semirational individual imitates its neighbor's strategy directly and adjusts its partnership based upon a simple reputation rule. Individual-based simulations show that cooperation cannot evolve in a population of all best responders even if they can switch their partners to somebody who can reward them best in game playing. However, when imitators exist, a stable community that consists of cooperative imitators emerges. Further, we show that a birth-death selection mechanism can eliminate all best responders, cultivating a social regime of all cooperative imitators. Compared with parallel simulations that assume fixed networks, cooperative imitators are evolutionarily favored, provided they are able to adjust their partners.

Preopening and Equilibrium Selection

We introduce a form of pre-play communication that we call for standard normal form, two player games. During the preopening players announce their strategy using a communication system which is subject to stochastic failures. Players payoffs depend on the action profile posted on the system at the end of the preopening. We show that the preopening allows players to coordinate on one pure strategy equilibrium of the game when the system failures hit the two players idiosyncratically. In such a case the preopening always leads the players to select the Pareto superior Nash equilibrium when this exhibits the maximum attainable payoff for each player. Moreover, in the class of two action games of conflicting interests the preopening leads to select the equilibrium preferred by the player with (i) the strongest preference over different equilibria, (ii) the lower cost of miscoordination or (iii) the less efficient posting system.