Game Theory

We investigate the extent to which price updates can increase the revenue of a seller with little prior information on demand. We study prior-free revenue maximization for a seller with unlimited supply of n item types facing m myopic buyers present for k < log n days. For the static (k = 1) case, Balcan et al. show that one random item price (the same on each item) yields revenue within a Θ(log m+log n) factor of optimum and this factor is tight. We introduce hereditary maximizers, a novel property regarding buyer valuations that is sufficient for a significant improvement of the approximation factor in the dynamic (k > 1) setting. The hereditary maximizers property limits complementarities among items and is satisfied by any multi-unit or gross substitutes valuation. Our main result is a non-increasing, randomized, schedule of k item prices, the same on each item, with expected revenue within a O( log m+log n k ) factor of optimum for private valuations with hereditary maximizers. This factor is tight (modulo a constant factor): we show that any pricing scheme over k days has a revenue approximation factor of at least log m+log n 3k . We obtain analogous matching lower and upper bounds of Θ( log n k ) if all valuations have the same maximum. We expect the technique we develop to prove our upper bound to be of broader interest; for example, it can be used to significantly improve over the result of . We also initiate the study of revenue maximization given allocative externalities (i.e. influences) between buyers with combinatorial valuations. We provide a rather general model of positive influence of others' ownership of items on a buyer's valuation. For affine, submodular externalities and valuations with hereditary maximizers we present an influence-and-exploit [13] marketing strategy based on our algorithm for private valuations. This strategy preserves our approximation factor, despite an affine increase (due to externalities) in the optimum revenue.

. The literature review brings us closer to understanding, it is of utmost importance for the construction and analysis of knowledge. The approach to these points arises from the vision of general science and is specifically directed to gerontology, which is a vehicle to transit through the objectives set out here. It is interesting to elucidate the construction of the origin of gerontology itself, not only as an etymological element but also from the formation of the discourse that gave rise to what would become the field of knowledge that it is today. The review of scientific literature allows us to recognize the importance and value of gerontology and, above all, its field of application, its contributions to the knowledge of older people seen, for example, from the life cycle. It allows those interested in the field raised here to recognize the double reading of old age, one from the biological sciences, the other from those of a social nature. When going through the intricate points of life, it does not seem strange to recognize the critical moments of the aging process, which constitutes a substantive part of the stages of life to ultimately become a matter for everyone, but not always understood in its entirety. In conclusion, it is possible to conclude on the obvious need to continue analyzing and constructing not only the necessary discourses but also the epistemological and philosophical principles of the study of old age and aging, understanding that gerontology is constituted not as a science in its own right but as a crucible that enriches a dynamic and inexhaustible scientific discipline.

In this letter, we address joint channel and power allocation in a device-to-device (D2D) network underlaying a cellular network in a time-varying environment. A fully distributed solution, which does not require information exchange, is proposed to allocate channel and power levels to D2D pairs while ensuring the quality of service (QoS) of the cellular user equipments (CUEs). The problem is modeled as a Stackelberg game with pricing. At the leader level, base station sets prices for the channels to ensure the QoS of the CUEs. At the follower level, D2D pairs use an uncoupled stochastic learning algorithm to learn the channel indices and power levels while minimizing the weighted aggregate interference and the price paid. The follower game is shown to be an ordinal potential game. We perform simulations to study the convergence of the algorithm.

Some major financial markets are currently reporting that 50% or more of all transactions are now executed by automated trading systems (ATS). To understand the impact of ATS proliferation on the global financial markets, academic studies often use standard reference strategies, such as "AA" and "ZIP", to model the behaviour of real trading systems. Disturbingly, we show that the reference algorithms presented in the literature are ambiguous, thus reducing the validity of strict comparative studies. As a remedy, we suggest disambiguated standard implementations of AA and ZIP. Using Exchange Portal (ExPo), an open-source financial exchange simulation platform designed for realtime behavioural economic experiments involving human traders and/or trader-agents, we study the e↵ects of disambiguating AA and ZIP, before introducing a novel method of assignment-adaptation (ASAD). Experiments show that introducing ASAD agents into a market with shocks can produce counter-intuitive market dynamics.

Carefully crafted computer worms such as Stuxnet and recent data breaches on retail organizations (e.g., Target, Home Depot) are very sophisticated security attacks on critical cyber infrastructures. Such attacks are referred to as advanced persistent threat (APT), and are on constant rise with severe implications. In all these attacks, the presence of an attacker itself is difficult to detect as they log-in as legitimate users. Hence, these attacks comprising multiple actions are challenging to differentiate from benign and therefore common detection techniques have to deal with high false positive rates. While machine learning and game theoretic models have been applied for intrusion detection, machine learning techniques lack the ability to model the rationality of the players, while the game theoretic approaches rely on the strict assumption of full rationality and complete information. This thesis discusses an approach that proposes Q-Learning to model the decision process of a security administrator which addresses the joint limitations of using game theory and machine learning techniques for this problem. This work compares variations of Q-Learning with a traditional stochastic game model by performing a simulation under different pair of profiles for attackers and defenders using parameters derived from real incident data of a large computer organization. Analysis on the strengths and weaknesses of the algorithms, and how the parameters in the algorithms affect the performance are studied. Simulation results show that Naive Q-Learning, despite the restricted information on the opponent, better reduces the impact of an attacker compared to Minmax Q-Learning against all attackers, or Stochastic Games players against less rational opponents.

The evolution of a system from the transient phase into a steady-state or asymptotic phase is an important area of study in engineering and the mathematical sciences. While analytic methods exist for determining the steady-state behavior of a system, the transient analysis is typically more difficult. Transient analysis is often approached in either an ad hoc, case-by-case manner or is performed by simulation. In this paper we explore the transient analysis of absorbing Markov chains by counting discrete-time events. We derive a closed-form expression for the expectation of these events and give some examples. We then show how several singleagent systems may be combined into a multi-agent system where the interactions between agents can be analyzed. This affords a model for analyzing competition. For example, we can determine advantages to specific players and determine the expected number of lead changes. After developing these ideas we present simulation results to verify our methods.

When starting a new collective venture, it is important to understand partners' motives and how strongly they commit to common goals. Arranging prior commitments or agreements on how to behave has been shown to be an evolutionary viable strategy in the context of cooperation dilemmas, ensuring high levels of mutual cooperation among self-interested individuals. However, in many situations, commitments can be used to achieve other types of collective behaviours such as coordination. Coordination is arguably more complex to achieve since there might be multiple desirable collective outcomes in a coordination problem (compared to mutual cooperation, the only desirable outcome in cooperation dilemmas), especially when these outcomes entail asymmetric benefits or payoffs for those involved. Using methods from Evolutionary Game Theory (EGT), herein we study how prior commitments can be adopted as a tool for enhancing coordination when its outcomes exhibit an asymmetric payoff structure. Our results, both by numerical simulations and analytically, show that whether prior commitment would be a viable evolutionary mechanism for enhancing coordination strongly depends on the collective benefit of coordination, and more importantly, how asymmetric benefits are resolved in a commitment deal.

As artificial intelligence (AI) systems are increasingly embedded in our lives, their presence leads to interactions that shape our behaviour, decision-making, and social interactions. Existing theoretical research has primarily focused on human-to-human interactions, overlooking the unique dynamics triggered by the presence of AI. In this paper, resorting to methods from evolutionary game theory, we study how different forms of AI influence the evolution of cooperation in a human population playing the one-shot Prisoner's Dilemma game in both well-mixed and structured populations. We found that Samaritan AI agents that help everyone unconditionally, including defectors, can promote higher levels of cooperation in humans than Discriminatory AI that only help those considered worthy/cooperative, especially in slow-moving societies where change is viewed with caution or resistance (small intensities of selection). Intuitively, in fast-moving societies (high intensities of selection), Discriminatory AIs promote higher levels of cooperation than Samaritan AIs.

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in copresence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents' implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours. In all cases, important open problems in MAS research as viewed or prioritised by the group are described.

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents’ implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to...

Both conventional wisdom and empirical evidence suggest that arranginga priorcommitment or agreement before an interaction takes place enhances the chance of reaching mutual cooperation. Yet it is not clear what mechanisms might underlie the participation in and compliance with such a commitment, especially when participation is costly and non-compliance can be profitable. Here, we develop a theory of participation and compliance with respect to an explicit commitment formation process and to institutional incentives where individuals, at first, decide whether or not to join a cooperative agreement to play a one-shot social dilemma game. Using a mathematical model, we determine whether and when participating in a costly commitment, and complying with it, is an evolutionarily stable strategy, resulting in high levels of cooperation. We show that, given a sufficient budget for providing incentives, rewarding of commitment compliant behaviours better promotes cooperation than punishmen...

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is...

Haldane accurately anticipates the conditions under which cooperation is the favorable choice in an interaction between genetic relatives. The general condition can later be formulated as a surprisingly simple mathematical expression, known as the Hamilton's rule, stating that natural selection favors cooperation if the genetic relatedness (r) between the donor and the recipient of a cooperative act is greater than its cost (c) to benefit (b) ratio [1]: r > c/b. Motivated by Hamilton's elegant early studies, researchers have attempted to find simple and concise rules that characterize the conditions for cooperation to be selected under various social viscosity . For example, the seminal work by M. Nowak in 2006 shows that similarly simple rules can be derived that govern each of the other four popular mechanisms of cooperation-direct reciprocity, indirect reciprocity, group selection and network reciprocity-, which can be expressed via the cost--to--benefit ratio being smaller than some critical value associated with the mechanism at work (as seen, for kin interactions, the critical value is relatedness). However, these rules are restricted to the donor and recipient (D&R) paradigm. The question is thus whether it is possible to obtain simple rules even for the general case? The answer is not trivial as a general two--player game is described by four independent parameters, not just two as in the D&R game. The excellent review by Wang et al. precisely addresses this question, capturing recent approaches and their pitfalls, and furthermore, proposing a novel approach tackling the pitfalls. The authors show that, using a new set of two scaling parameters (see precise definitions of D g ' and D r ' in Equation ( ) of the review), one can consistently characterize the conditions for the emergence and stability of cooperation in the general pairwise game when social viscosity is introduced through any of the five aforementioned mechanisms. Using theoretical analysis and simulations, the validity of the approach is demonstrated for both finite and infinite, well--mixed populations, as well as for various updating rules in structured populations. As such, the authors have proved that the new approach is better than all existing ones as the latter fail to produce consistent results when either social viscosity or population structure is considered. It is noteworthy that all existing approaches reduce the four parameter set that defines the general game to a reduced set of two scaling parameters. As an application of the newly introduced approach, the authors demonstrate that the paradox of cooperation

In Evolutionary Biology and Game Theory, there is a long history of models aimed at predicting strategies adopted by agents during resource foraging. In Artificial Life, the agentbased modeling approach allowed to simulate the evolution of foraging behaviors in populations of artificial agents embodied in a simulated environment. In this paper, different sets of behaviors are evolved from a simple setting where agents seek for food patches distributed on a two-dimensional map. While agents are not explicitly playing a game of chicken, their strategies are found on a spectrum ranging from a frugal strategy (aka Dove) to a greedy strategy (aka Hawk). This phenomenon is due to the fact that moving is both a way for the agents to play or go to get away from an unfavorable area of the environment. It is also observed that by moving away, the agents preserve the ecology, preventing the resource from disappearing locally. Those strategies are shown to be stable if the environment is colonized by one given population. However, post-mortem tournaments among different groups of agents (separately evolved), systematically result in a specific group of agents dominating. The optimal strategy in the simulated tournaments is found to be one with fine-tuned timing for leaving. Further analysis shows how the strategy exploits resources without completely depleting them, producing Volterra-like population tendencies.

The role of imperfect information in a principal-agent relationship subject to moral hazard is considered. A necessary and sufficient condition for imperfect information, to improve on contracts based on the payoff alone is derived, and a characterization of the optimal use of such information is given.

A challenge for future user-provided networks will be reconciling potentially conflicting demands for a finite resource, such as network bandwidth. In this position paper, we discuss how this problem is tackled in an operational communitydriven wireless mesh network. A key outcome of this discussion is that, although the approach that has evolved is not ideal, it allows the reconciliation of conflicting demands for use of the shared network to reflect communal concerns; a property we believe is essential to its success, and that of user-provided networks. We argue that user-driven distributed arbitration of requests for resource is necessary in a user-provided network, and via a simple abstraction we discuss design options to enable this. The consequences of incorrect design decisions could negatively impact a network and its community of users. To help us make appropriate design decisions, we could look to scientific methods, such as game theory. However, we find them unable to model the intricacies of communal life, leading us to suggest it is necessary to stop considering users as anonymous rational persons and to start factoring in their personalities and beliefs.

A major evolutionary question is how reproductive sharing arises in cooperatively breeding species despite the inherent reproductive conflicts in social groups. Reproductive skew theory offers one potential solution: each group member gains or is allotted inclusive fitness equal to or exceeding their expectation from reproducing on their own. Unfortunately, a multitude of skew models with conflicting predictions has led to confusion in both testing and evaluating skew theory. The confusion arises partly because one set of models (the 'transactional' type) answer the ultimate evolutionary question of what ranges of reproductive skew can yield fitness-enhancing solutions for all group members. The second set of models ('compromise') give an evolutionarily proximate, game-theoretic evolutionarily stable state (ESS) solution that determines reproductive shares based on relative competitive abilities. However, several predictions arising from compromise models require a linear payoff to increased competition and do not hold with non-linear payoffs. Given that for most species it may be very difficult or impossible to determine the true relationship between effort devoted to competition and reproductive share gained, compromise models are much less predictive than previously appreciated. Almost all skew models make one quantitative prediction (e.g. realized skew must fall within ranges predicted by transactional models), and two qualitative predictions (e.g. variation in relatedness or competitive ability across groups affects skew). A thorough review of the data finds that these three predictions are relatively rarely supported. As a general rule, therefore, the evolution of cooperative breeding appears not to be dependent on the ability of group members to monitor relatedness or competitive ability in order to adjust their behaviour dynamically to gain reproductive share. Although reproductive skew theory fails to predict within-group dynamics consistently, it does better at predicting quantitative differences in skew across populations or species. This suggests that kin selection can play a significant role in the evolution of sociality. To advance our understanding of reproductive skew will require focusing on a broader array of factors, such as the frequency of mistaken identity, delayed fitness payoffs, and selection pressures arising from across-group competition. We furthermore suggest a novel approach to investigate the sharing of reproduction that focuses on the underlying genetics of skew. A quantitative genetics approach allows the partitioning of variance in reproductive share itself or that of traits closely associated with skew into genetic and non-genetic sources. Thus, we can determine the heritability of reproductive share and infer whether it actually is the focus of natural selection. We view the 'animal model' as the most promising empirical method where the genetics of reproductive share can be directly analyzed in wild populations. In the quest to assess whether skew theory can provide a framework for understanding the evolution of sociality, quantitative genetics will be a central tool in future research.

We present automatic verification techniques for the modelling and analysis of probabilistic systems that incorporate competitive behaviour. These systems are modelled as turn-based stochastic multi-player games, in which the players can either collaborate or compete in order to achieve a particular goal. We define a temporal logic called rPATL for expressing quantitative properties of stochastic multi-player games. This logic allows us to reason about the collective ability of a set of players to achieve a goal relating to the proba-bility of an event’s occurrence or the expected amount of cost/reward accumulated. We give an algorithm for verifying properties expressed in this logic and implement the techniques in a probabilistic model checker, as an extension of the PRISM tool. We demonstrate the ap-plicability and efficiency of our methods by deploying them to analyse and detect potential weaknesses in a variety of large case studies, including algorithms for energy management in...

This paper considers one machine job scheduling situations or sequencing problems, where clients can have more than a single job to be processed in order to get a final output. Moreover, a job can be of interest for different players. This means that one of the main assumptions in classic sequencing problems is dropped: the one to one correspondence between clients and jobs. It is shown that the corresponding cooperative games are balanced for specific types of cost criteria.

We consider competition between non-cooperative mobiles over several independent collision multiple-access channels or more generally, over interference channels. A mobile selects one of the channels for transmission; if the same channel is chosen simultaneously by more than a single mobile then a collision occurs and the colliding packets are lost. Policies based on assigning each independent channel to another mobile turn out to constitute efficient equilibria. But they request some synchronization and coordination which may not be possible. We identify other mixed equilibria that do not request synchronization but exhibit innefficiency. Moreover, we show that a Braess type paradox occurs in which the availability of more resources results in poorer performance to all mobiles. We extend the model to investigate the influence of capture phenomena.

This paper presents a new, multidimensional comprehensive equilibrium theory that goes beyond classical theories such as Nash equilibrium and behavioral game theory equilibrium, by integrating rational factors (such as economic losses and gains) with irrational factors (such as fear, biases, public opinion, and misinformation). The theory aims to achieve an equilibrium that can be applied to various fields including international crises, health policies, economic decisions, and even daily life, to provide the best collective decision that contributes to the stability of the system in question. We will present the basic concepts on which this theory is based, a detailed critique of previous theories with examples and mathematics, a comprehensive definition of the new theory and its basic equation, an explanation of the mechanisms for determining values and weights, and multiple applications of the theory with mathematical examples to compare it with Nash equilibrium.

In this thesis, we study the problems of risk measurement, valuation and hedging of financial positions in incomplete markets when an insufficient number of assets are available for investment (real options). We work closely with three measures of risk: Worst-Case Scenario (WCS) (the supremum of expected values over a set of given probability measures), Value-at-Risk (VaR) and Average Value-at-Risk (AVaR), and analyse the problem of hedging derivative securities depending on a non-traded asset,

In the context of voting with ranked ballots, an important class of voting rules is the class of margin-based rules (also called pairwise rules). A voting rule is margin-based if whenever two elections generate the same head-to-head margins of victory or loss between candidates, then the voting rule yields the same outcome in both elections. Although this is a mathematically natural invariance property to consider, whether it should be regarded as a normative axiom on voting rules is less clear. In this paper, we address this question for voting rules with any kind of output, whether a set of candidates, a ranking, a probability distribution, etc. We prove that a voting rule is margin-based if and only if it satisfies some axioms with clearer normative content. A key axiom is what we call Preferential Equality, stating that if two voters both rank a candidate x immediately above a candidate y, then either voter switching to rank y immediately above x will have the same effect on the election outcome as if the other voter made the switch, so each voter's preference for y over x is treated equally.

In this thesis, we study the problems of risk measurement, valuation and hedging of financial positions in incomplete markets when an insufficient number of assets are available for investment (real options). We work closely with three measures of risk: Worst-Case Scenario (WCS) (the supremum of expected values over a set of given probability measures), Value-at-Risk (VaR) and Average Value-at-Risk (AVaR), and analyse the problem of hedging derivative securities depending on a non-traded asset,

A handlebody link is a union of handlebodies of positive genus embedded in 3-space, which generalizes the notion of links in classical knot theory. In this paper, we consider handlebody links with one genus 2 handlebody and n−1 solid tori, n &gt; 1. Our main result is the complete classification of such handlebody links with six crossings or less, up to ambient isotopy.

We report on an experiment comparing two institutions for determining spending on public goods. Two parties bargain over how to divide a dollar between a public and two private accounts, one for each party. Each party attaches a different weight to the public good, either high or low, and there is some probabilistic persistence over time as to which party makes a proposal. Under discretion, the status quo default allocation to the public account (in the event of disagreement) is zero while under a mandatory rule it is equal to the level that was last agreed upon. The mandatory rule thus creates a relationship between current decisions and future payoffs, and our experiment tests the theoretical prediction that the efficient level of public good is provided under the mandatory rule while the level of public good funding is at a sub-optimal level under the discretionary rule. We find that proposers (particularly high types) tend to contribute significantly more to the public account under the mandatory institution than under the discretionary rule, and that the dynamic patterns of adjustment to the public account are largely consistent with theoretical predictions.

introduce the weakest link against the best shot property to the Colonel Blotto game where the defendant has to win all the battle fields while the attacker only needs to win at least one battlefield. They characterize the Nash equilibrium assuming that the attacker attacks all the battlefields simultaneously. We construct a two stage model and endogenize the attacker's attack pattern. We show that the attacker chooses a sequential attack pattern in the subgame perfect Nash equilibrium. Therefore, the game analyzed by Clark and Konrad ( ) is never realized. We also conducted experiments and found that the subjects' behavior was inconsistent to theoretical predictions. Both players overinvested and the variances were large. In the simultaneous game, the attackers took a guerrira strategy at 30% of the time in which they invested only in one battlefield and the defenders took a surrender strategy at 11% of the time in which they invested nothing in the simultaneous game. Both players invested more in period 1 than in period 2 in the sequential game. Although all of these are inconsistent to the theoretical predictions, the winning probability of a game was consistent to the theoretical prediction in the simultaneous games, but it was lower than the theoretical prediction in the sequential games. We conclude that the subjects' irrational behavior is mainly a rational response to his/ her opponent's irrational behavior. Our model can explain terrorism, cyber terrorism, lobbying, and patent trolls and the huge gap between the theory and the experiments are important considering the significance of the problems.

Clark and Konrad (2007) introduce the weakest link against the best shot property to the Colonel Blotto game where the defendant has to win all the battle fields while the attacker only needs to win at least one battlefield. They characterize the Nash equilibrium assuming that the attacker attacks all the battlefields simultaneously. We construct a two stage model and endogenize the attacker’s attack pattern. We show that the attacker chooses a sequential attack pattern in the subgame perfect Nash equilibrium. Therefore, the game analyzed by Clark and Konrad (2007) is never realized. We also conducted experiments and found that the subjects’ behavior was inconsistent to theoretical predictions. Both players overinvested and the variances were large. In the simultaneous game, the attackers took a guerrira strategy at 30% of the time in which they invested only in one battlefield and the defenders took a surrender strategy at 11% of the time in which they invested nothing in the simul...

and A 2 decide to invest simultaneously. b.-Companies A 1 and A 2 decide to wait. c.-Company A 1 (the leader) decides to invest and company A 2 (the follower) decides to wait. And d.-Company A 1 decides to wait and company A 2 decides to invest. Having four tactics in each node, the solution with the highest pay-offs or best responses are called Nash equilibrium (who gives the solution) for a game between two players. Figure No. 2 show the different games and solutions when two companies participate in the game.

The Shapley value for nonatomic games is used to determine the shares of equity and risky debt in the value of a firm facing uncertain growth opportunities. We include the "voting power" attached to equity holdings and focus on the bargaining between shareholders and debtholders, where the two classes may overlap, obtaining an extension of the Modigliani-Miller theorem.

For the air traffic management cyber physical system, if an attacker successfully obtains authority or data through a cyber attack, combined with physical attacks, it will cause serious consequences. Game theory can be applied to the strategic interaction between two parties, especially if the two parties have different goals. The offensive and defensive game process of the air traffic management cyber physical system is a non-cooperative and incomplete information dynamic game. The attacker can choose to camouflage the type of attack launched. The attack detection device configured in the system has a certain probability that the attack type can be successfully detected. According to the type of attack detected, the defender updates the posterior belief of the attack type and selects the corresponding protective strategies. According to the game process of offense and defense, a dynamic Bayesian game model of the air traffic management cyber physical system is established, the poss...

In evolutionary game theory, a relative comparison of the cost and benefit associated with obtaining a resource, called payoff, is used as an indicator of fitness of an organism. Such payoff matrices are used to understand complex inter-species and intra-species interactions like cooperation, mutualism, and altruism. In the absence of any empirical data, the evolution of these payoff matrices has been investigated theoretically by tweaking well-established game theory models. In this paper, we present empirical evidence of three types of resource-dependent changes in the payoff matrices of evolving Saccharomyces cerevisiae populations. We show that depending on the carbon source and participating genotypes, the payoff matrix could either (a) evolve quantitatively yet maintain a cheater-cooperator game, (b) change qualitatively such that the cheater-cooperator game collapses, or (c) change qualitatively to result in the birth of a cheater-cooperator game. Our results highlight the need to consider the dynamic nature of payoff matrices while making even short-term predictions about population interactions and dynamics.

Mobile Adhoc Network (MANET) has been a core topic of research since the last decade. Currently, this form of networking paradigm is increasingly being construed as an integral part of upcoming urban applications of Internet-of-Things (IoT), consisting of massive connectivity of diverse types of nodes. There is a significant barrier to the applicability of existing routing approaches in conventional MANETs when integrated with IoT. This routing mismatch can lead to security risks for the MANET-based application tied with the IoT platform. This paper examines a pragmatic scenario as a test case wherein the mobile nodes must exchange multimedia signals for supporting real-time streaming applications. There exist two essential security requirements viz. i) securing the data packet and ii) understanding the unpredictable behavior of the attacker. The current study considers sophistication on the part of attacker nodes. They are aware of each other's identity and thereby collude to conduct lethal attacks, which is rarely reflected in existing security modeling statistics. This research harnesses the potential modeling aspect of game theory to model the multiple-collusion attacker scenario. It contributes towards i) modeling strategies of regular/malicious nodes and ii) applying optimization principle using novel auxiliary information to formulate the optimal strategies. The model advances each regular node's capability to carry out precise computation about the opponent player's strategy prediction, i.e., malicious node. The simulation outcome of the proposed mathematical model in MATLAB ascertains that it outperforms the game theory's baseline approach. INDEX TERMS Colluder, game theory, mobile adhoc network, multi-collusion attacker, multistage game, secure routing.

Game Theory acts as a suitable tool offering promising solutions to security -related concerns in Mobile Ad Hoc Networks (i.e., MANETs ). In MANETs , security forms a prominent concern as it includes nodes which are usually portable and require significant coordination between them. Further, the absence of physical organisation makes such networks susceptible to security breaches, hindering secure routing and execution among nodes. Game Theory approach has been manipulated in the current study to achieve an analytical view while addressing the security concerns in MANETs . This paper offers a Bayesian -Signaling game model capable of analysing the behaviour associated with regular as well as malicious nodes. In the proposed model , the utility of normal nodes has been increased while reducing the utility linked to malicious nodes. Moreover, the system employs a reputation system capable of stimulating best cooperation between the nodes. The regular nodes record incessantly to examine their corresponding nodes' behaviours by using the belief system of Bayes-rules. On its comparison with existing schemes, it was revealed that the presented algorithm provides better identification of malicious nodes and attacks while delivering improved throughput and reduced false positive rate.

The progressively ubiquitous connectivity in the present information systems pose newer challenges to security. The conventional security mechanisms have come a long way in securing the well-defined objectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in the system complexities and attack sophistication, providing security via traditional means are increasingly becoming unachievable. A novel theoretical perspective and an innovative approach are thus required for understanding security from a decision-making and strategic viewpoint. One of the analytical tools which may assist the researchers in designing security protocols for computer networks is game theory. The game-theoretic concept finds extensive applications in security at different levels, including the cyberspace and is generally categorized under security games. It can be utilized as a robust mathematical tool for modelling and analyzing contemporary security issues. Game theory offers a natural framework for capturing the defensive as well as adversarial interactions between the defenders and the attackers. Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategies of attackers by equilibrium evaluation of the security games. In this paper, the concept of game theory has been presented, followed by game-theoretic applications in cybersecurity, including cryptography. Different types of games, particularly those focused on securing the cyberspace, have been analysed and varied game-theoretic methodologies including mechanism design theories have been outlined for offering a modern foundation of the science of cybersecurity.

The Mobile Ad-Hoc Network (MANET) incorporates a collaborative networking scenario, where dynamic host movement results in frequent topology changes. In MANET, nodes cooperate during route establishment, and the data packet must travel from source to destination through multi-hop intermediate links. The nodes in a MANET can be localized in a restricted zone, where manual intervention to set-up fixed infrastructural support is practically infeasible. However, cooperative packet forwarding and data transmission is quite a common scenario in the context of MANET. Still, due to dynamic topological changes, weak, intermittent links appear within one-hop communication. This leads to a higher possibility of packet drop events and also increases the retransmission scenario, which affects the energy performance of the network. Addressing this issue, the study models a novel and intelligent packet forwarding approach based on the game theory, where trust evaluation in terms of node reputation factor also plays a very vital role. The approach also enforces an incentive modelling to stimulate the cooperation between mobile nodes during the MANET routing scenario. The system is designed and developed with evolutionary game perspectives to meet the Quality of Services (QoS) requirements. The experimental analysis supports the proposed modelling design aspects. Also, it exhibits that the reputation and trust-based game increases the utility of packet-forwarding strategy with high throughput and negligible network overhead. INDEX TERMS Cooperative packet forwarding, game theory, intermittent link failure, mobile adhoc networks (MANET), reliable data transmission.

a b s t r a c t 29 Intratumoral heterogeneity (ITH) has been regarded as a key cause of the failure and resistance of cancer 30 therapy, but how it behaves and functions remains unclear. Advances in single-cell analysis have facili-31 tated the collection of a massive amount of data about genetic and molecular states of individual cancer 32 cells, providing a fuel to dissect the mechanistic organization of ITH at the molecular, metabolic and posi-33 tional level. Taking advantage of these data, we propose a computational model to rewire up a topological 34 network of cell-cell interdependences and interactions that operate within a tumor mass. The model is 35 grounded on the premise of game theory that each interactive cell (player) strives to maximize its fitness 36 by pursuing a ''rational self-interest" strategy, war or peace, in a way that senses and alters other cells to 37 respond properly. By integrating this idea with genome-wide association studies for intratumoral cells, 38 the model is equipped with a capacity to visualize, annotate and quantify how somatic mutations medi-39 ate ITH and the network of intratumoral interactions. Taken together, the model provides a topological 40 flow by which cancer cells within a tumor cooperate or compete with each other to downstream patho-41 genesis. This topological flow can be potentially used as a blueprint for genetically intervening the pat-42 tern and strength of cell-cell interactions towards cancer control. 43

Linked econometric models can often be viewed upon as a difference game. In this paper we study linear quadratic difference games. The Pareto optimal strategies and sufficient conditions for the Nash optimal strategy to be Pareto optimal will be given.

In this work, an integrated Game Theory (GT) approach is developed for the coordination of multienterprise Supply Chains (SCs) in a competitive uncertain environment. The conflicting goals of the different participants are solved through coordination contracts using a non-cooperative non-zero-sum Stackelberg game under the leadership of the manufacturer. The Stackelberg payoff matrix is built under the nominal conditions, and then evaluated under different probable uncertain scenarios using a Monte-Carlo simulation. The competition between the Stackelberg game players and the third parties is solved through a Nash Equilibrium game. A novel way to analyze the game outcome is proposed based on a win-win Stackelberg set of "Pareto-frontiers". The benefits of the resulting MINLP tactical models are illustrated by a case study with different vendors around a client SC. The results show that coordinated decisions lead to higher expected payoffs compared to the standalone case, while also leading to uncertainty reduction.

In this paper, we model socially-embedded games. We use non-cooperative game forms with pure strategy Nash equilibria and embed them through framing structures. These frames alter how players perceive the game, or rule out the choice of some elements of players' option sets. In this way, we explicitly link the notion of social game to concepts taken from Erving Goffman's theory of interaction. According to Goffman, game theory is flawed because it applies a single-level model to two-leveled situations, ignoring the fact that people form impressions and expectations during or before any strategic game. In this essay, we provide a set-up endowed with these two levels. Firstly, players endogenize a given setting and frame the interaction. This determines what kind of game they will play. Secondly, they select Nash equilibrium strategies. As we shall discuss, it is possible in this way to consider how frames operate and what role they have in determining Nash solutions of non-cooperative games.

In this paper, we model socially-embedded games. We use non-cooperative game forms with pure strategy Nash equilibria and embed them through framing structures. These frames alter how players perceive the game, or rule out the choice of some elements of players' option sets. In this way, we explicitly link the notion of social game to concepts taken from Erving Goffman's theory of interaction. According to Goffman, game theory is flawed because it applies a single-level model to two-leveled situations, ignoring the fact that people form impressions and expectations during or before any strategic game. In this essay, we provide a set-up endowed with these two levels. Firstly, players endogenize a given setting and frame the interaction. This determines what kind of game they will play. Secondly, they select Nash equilibrium strategies. As we shall discuss, it is possible in this way to consider how frames operate and what role they have in determining Nash solutions of non-cooperative games.

This paper presents new cooperative path planning algorithms for multiple unmanned aerial vehicles (UAVs) using Game theory-based particle swarm optimization (GPSO). First, the formation path planning is formulated into the minimization of a cost function that incorporates multiple objectives and constraints for each UAV. A framework based on game theory is then developed to cast the minimization into the problem of finding a Stackelberg-Nash equilibrium. Next, hierarchical particle swarm optimization algorithms are developed to obtain the global optimal solution. Simulation results show that the GPSO algorithm can generate efficient and feasible flight paths for multiple UAVs, outperforming other path planning methods in terms of convergence rate and flexibility. The formation can adjust its geometrical shape to accommodate a working environment. Experimental tests on a group of three UAVs confirm the advantages of the proposed approach for a practical application. INDEX TERMS Cooperative path planning, UAV, Stackelberg-Nash game, PSO.

Along with rapid economic growth in Indonesia, Container throughput of Indonesia's Port also increasing. The container throughput of Tanjung Priok decrease on average 7.8% 2012 -2015. The CAGR of container throughput 2015 -2016 is 9.5%. PT. JICT and TPK Koja are located at Tanjung Priok Port so that the terminal location is close together, so the hinterlands between JICT and TPK Koja are the same. The rapidly growing container trends encourage the development of container terminals to compete in providing services and facilities. For example JICT is superior in berth length, it has a pier length of 1,640 m while the Koja TPK only has a pier length of 650 m then In terms of JICT container yard capacity is superior that is equal to 39,884 TEUs while Koja TPK is only 15,456 TEUs, in terms of the water depth JICT and TPK Koja have 14 mLWS. The creation of inter-port competition can be marked by differences in the number of International containers throughput. in 2014 JICT has 2,372,470 TEUs international container throughput and KSO Koja TPK has 587,712 TEUs international container throughput. In 2015 JICT's throughput is decrease. The throughput is 2,223,179 while the KSO TPK Koja increased to 648,373 TEUs. In 2016 JICT's throughput decreases again to be 2,144,398 TEUs and for KSO TPK Koja also decline to be 561,895 TEUs. This research is conducted to determine the port competition with game theory approach. The result JICT has 53% market share and TPK Koja has 47%.