Selfish Agents Research Articles

Non-equilibrium time-dependent solution to discrete choice with social interactions.

We solve the binary decision model of Brock and Durlauf (2001) in time using a method reliant on the resolvent of the master operator of the stochastic process. Our solution is valid when not at equilibrium and can be used to exemplify path-dependent behaviours of the binary decision model. The solution is computationally fast and is indistinguishable from Monte Carlo simulation. Well-known metastable effects are observed in regions of the model's parameter space where agent rationality is above a critical value, and we calculate the time scale at which equilibrium is reached using a highly accurate method based on first passage time theory. In addition to considering selfish agents, who only care to maximise their own utility, we consider altruistic agents who make decisions on the basis of maximising global utility. Curiously, we find that although altruistic agents coalesce more strongly on a particular decision, thereby increasing their utility in the short-term, they are also more prone to being subject to non-optimal metastable regimes as compared to selfish agents. The method used for this solution can be easily extended to other binary decision models, including Kirman's model of ant recruitment Kirman (1993), and under reinterpretation also provides a time-dependent solution to the mean-field Ising model. Finally, we use our time-dependent solution to construct a likelihood function that can be used on non-equilibrium data for model calibration. This is a rare finding, since often calibration in economic agent based models must be done without an explicit likelihood function. From simulated data, we show that even with a well-defined likelihood function, model calibration is difficult unless one has access to data representative of the underlying model.

Network Aggregative Game in Unknown Dynamic Environment With Myopic Agents and Delay

In this article, the framework of network aggregative game (NAG) is extended by considering the interaction of players in a dynamic environment whose model is not known to them. The cost function of each player depends on its own strategy, the aggregate strategy of its neighbor players in the network, and also the state of the environment. The state of the environment evolves with a dynamics that is affected by the strategies of players as an input to the model. The players of NAG are modeled as myopic selfish agents who observe the decision value of their neighbors and the state of the environment with some time delay and, then, adjust their decisions using a projected subgradient rule. The main contribution of this article is to provide conditions in which the strategies of myopic agents converge to the unique fixed-point Nash equilibrium point of the proposed dynamic NAG. Furthermore, as a case study, the framework is applied to a wireless network by modeling the service providers as the agents and evolution of service selection by users as the dynamics of the environment.

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Abstract Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

Bounding Residence Times for Atomic Dynamic Routings

In this paper, we are concerned with bounding agents’ residence times in the network for a broad class of atomic dynamic routings. We explore novel token techniques to circumvent direct analysis on complicated chain effects of dynamic routing choices. Even though agents may enter the network over time for an infinite number of periods, we prove that under a mild condition, the residence time of every agent is upper bounded (by a network-dependent constant plus the total number of agents inside the network at the entry time of the agent). Applying this result to three game models of atomic dynamic routing in the recent literature, we establish that the residence times of selfish agents in a series-parallel network with a single origin-destination pair are upper bounded at equilibrium, provided the number of incoming agents at each time point does not exceed the network capacity (i.e., the smallest total capacity of edges in the network whose removal separates the origin from the destination).

Multi-agent reinforcement learning for Markov routing games: A new modeling paradigm for dynamic traffic assignment

This paper aims to develop a paradigm that models the learning behavior of intelligent agents (including but not limited to autonomous vehicles, connected and automated vehicles, or human-driven vehicles with intelligent navigation systems where human drivers follow the navigation instructions completely) with a utility-optimizing goal and the system’s equilibrating processes in a routing game among atomic selfish agents. Such a paradigm can assist policymakers in devising optimal operational and planning countermeasures under both normal and abnormal circumstances. To this end, we develop a Markov routing game (MRG) in which each agent learns and updates her own en-route path choice policy while interacting with others in transportation networks. To efficiently solve MRG, we formulate it as multi-agent reinforcement learning (MARL) and devise a mean field multi-agent deep Q learning (MF-MA-DQL) approach that captures the competition among agents. The linkage between the classical DUE paradigm and our proposed Markov routing game (MRG) is discussed. We show that the routing behavior of intelligent agents is shown to converge to the classical notion of predictive dynamic user equilibrium (DUE) when traffic environments are simulated using dynamic loading models (DNL). In other words, the MRG depicts DUEs assuming perfect information and deterministic environments propagated by DNL models. Four examples are solved to illustrate the algorithm efficiency and consistency between DUE and the MRG equilibrium, on a simple network without and with spillback, the Ortuzar Willumsen (OW) Network, and a real-world network near Columbia University’s campus in Manhattan of New York City.

Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions.

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.

Selfish risk-seeking can provide an evolutionary advantage in a conditional public goods game.

While cooperation and risk aversion are considered to be evolutionarily advantageous in many circumstances, and selfish or risky behaviour can bring negative consequences for individuals and the community at large, selfish and risk-seeking behaviour is still often observed in human societies. In this paper we consider whether there are environmental and social conditions that favour selfish risk-seeking individuals within a community and whether tolerating such individuals may provide benefits to the community itself in some circumstances. We built an agent-based model including two types of agent—selfish risk-seeking and generous risk-averse—that harvest resources from the environment and share them (or not) with their community. We found that selfish risk-seekers can outperform generous risk-averse agents in conditions where their survival is moderately challenged, supporting the theory that selfish and risk-seeking traits combined are not dysfunctional but rather can be evolutionarily advantageous for agents. The benefit for communities is less clear, but when generous agents are unconditionally cooperative communities with a greater proportion of selfish risk-seeking agents grow to a larger population size suggesting some advantage to the community overall.

Multipopulation Aggregative Games:Equilibrium Seeking via Mean-Field Control and Consensus

In this article, we extend the theory of deterministic mean-field/aggregative games to multipopulation games. We consider a set of populations, each managed by a population coordinator (PC), of selfish agents playing a global noncooperative game, whose cost functions are affected by an aggregate term across all agents from all populations. In particular, we impose that the agents cannot exchange information between themselves directly; instead, only a PC can gather information on its own population and exchange local aggregate information with the neighboring PCs. To seek an equilibrium of the resulting (partial-information) game, we propose an iterative algorithm where each PC broadcasts a mean-field signal, namely, an estimate of the overall aggregative term, to its own population only. In turn, we let the local agents react with the best response and the PCs cooperate for estimating the aggregative term. Our main technical contributions are to cast the proposed scheme as a fixed-point iteration with errors, namely, the interconnection of a Krasnoselskij–Mann iteration and a linear consensus protocol, and, under a nonexpansiveness condition, to show convergence towards an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -Nash equilibrium, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is inversely proportional to the population size.

Collective Strategies With a Master-Slave Mechanism Dominate in Spatial-Iterated Prisoner's Dilemma

How cooperation emerges and persists in a population of selfish agents is a fundamental question in evolutionary game theory. The research shows that collective strategies with master-slave mechanism (CSMSM) defeat tit-for-tat and other well-known strategies in spatial iterated prisoner's dilemma. A CSMSM identifies kin members by means of a handshaking mechanism. If the opponent is identified as non-kin, a CSMSM will always defect. Once two CSMSMs meet, they play master and slave roles. A mater defects and a slave cooperates in order to maximize the master's payoff. CSMSM outperforms non-collective strategies in spatial IPD even if there is only a small cluster of CSMSMs in the population. The existence and performance of CSMSM in spatial iterated prisoner's dilemma suggests that cooperation first appears and persists in a group of collective agents.

A Distributed Algorithm for Large-Scale Linearly Coupled Resource Allocation Problems with Selfish Agents

A decentralized randomized coordinate descent method is proposed to solve a large-scale linearly constrained, separable resource optimization problem with selfish agent. This method has a cheap computational cost and can guarantee an improvement of selected objective function without jeopardizing the others in each iteration. The convergence rate is obtained using an alternative gap benchmark of objective value. Numerical simulations suggest that the algorithm will converge to a random point on the Pareto front.

Selfish Creation of Social Networks

Understanding real-world networks is a core research endeavor within the last two decades. Network Creation Games are a promising approach for this from a game-theoretic perspective. In these games, selfish agents corresponding to nodes in a network strategically decide which links to form to optimize their centrality. Many versions have been introduced and analyzed, but none of them fits to modeling the evolution of social networks. In real-world social networks connections are often established by recommendations from common acquaintances or by a chain of such recommendations. Thus establishing and maintaining a contact with a friend of a friend is easier than connecting to complete strangers. This explains the high clustering, i.e., the abundance of triangles, in real-world social networks. We propose and analyze a network creation model inspired by real-world social networks. In our model edges are formed via bilateral consent of both endpoints and the cost for establishing and maintaining an edge is proportional to the distance of the endpoints before establishing the connection. We provide results for generic cost functions which essentially only must be convex functions in the distance of the endpoints without the respective edge. For this broad class of cost functions we provide many structural properties of equilibrium networks and prove (almost) tight bounds on the diameter, the Price of Anarchy and the Price of Stability. Moreover, as a proof-of-concept we show via experiments that the created equilibrium networks of our model indeed closely mimic real-world social networks. We observe degree distributions that seem to follow a power-law, high clustering, and low diameters. This can be seen as a promising first step towards game-theoretic network creation models that predict networks featuring all core real-world properties.

Strategyproof Mechanisms for Additively Separable and Fractional Hedonic Games

Additively separable hedonic games and fractional hedonic games have received considerable attention in the literature. They are coalition formation games among selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions into coalitions) assuming that preferences are given. However, there is little discussion of this assumption. In fact, agents receive different utilities if they belong to different coalitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions.

Mechanistically comparing reproductive manipulations caused by selfish chromosomes and bacterial symbionts.

Insects naturally harbor a broad range of selfish agents that can manipulate their reproduction and development, often leading to host sex ratio distortion. Such effects directly benefit the spread of the selfish agents. These agents include two broad groups: bacterial symbionts and selfish chromosomes. Recent studies have made steady progress in uncovering the cellular targets of these agents and their effector genes. Here we highlight what is known about the targeted developmental processes, developmental timing, and effector genes expressed by several selfish agents. It is now becoming apparent that: (1) the genetic toolkits used by these agents to induce a given reproductive manipulation are simple, (2) these agents target sex-specific cellular processes very early in development, and (3) in some cases, similar processes are targeted. Knowledge of the molecular underpinnings of these systems will help to solve long-standing puzzles and provide new tools for controlling insect pests.

Consensus decision-making: performance of heuristics and mental models

Abstract Consensus decision-making, found in settings ranging from formal institutions to ad hoc groups, represents a critical component of human social interaction. In such cases, decision-makers often must agree to a course of action with awareness of others' behavior (e.g., votes) but not group members' underlying motivations and strategies. How, then, should individual agents/decision-makers balance the payoffs of available options against the time it takes to reach consensus (ranging from quick consensus to stalemate)? In the current research, a novel, repeated consensus task was played by simulated agents. These agents differed in how they a) evaluated the available options and b) anticipated the choices of fellow group members. While intuition may suggest that intractably selfish agents will outcompete those willing to compromise, the data demonstrate that socially minded agents – i.e., one type that employed a simple heuristic and two types that employed sophisticated social cognition – were the only ones to exhibit evolutionarily stable strategies. However, sophisticated cognition did not guarantee good performance, suggesting that caution is warranted in trying to “outsmart” competing agents. While these agents were restricted to simple behaviors, the approach and models described here provide a potentially useful framework for studying consensus in humans.

Network Creation Games with Traceroute-Based Strategies

Network creation games have been extensively used as mathematical models to capture the key aspects of the decentralized process that leads to the formation of interconnected communication networks by selfish agents. In these games, each user of the network is identified by a node and selects which link to activate by strategically balancing his/her building cost with his/her usage cost (which is a function of the distances towards the other player in the network to be built). In these games, a widespread assumption is that players have a common and complete information about the evolving network topology. This is only realistic for small-scale networks as, when the network size grows, it quickly becomes impractical for the single users to gather such a global and fine-grained knowledge of the network in which they are embedded. In this work, we weaken this assumption, by only allowing players to have a partial view of the network. To this aim, we borrow three popular traceroute-based knowledge models used in network discovery: (i) distance vector, (ii) shortest-path tree view, and (iii) layered view. We settle many of the classical game theoretic questions in all of the above models. More precisely, we introduce a suitable (and unifying) equilibrium concept which we then use to study the convergence of improving and best response dynamics, the computational complexity of computing a best response, and to provide matching upper and lower bounds to the price of anarchy.

Economics and the Social Thought of the Catholic Church

The economic theory we know today has been in development since the XVIII century. Physiocrats, Adam Smith, David Ricardo, and Thomas Malthus developed a theory that assumes that economy is governed by natural laws themselves, free competition, individual interest, and a laissez-faire guided the State to protect private property. In the second half of the XIX century, Marx demonstrated the weaknesses of the classical model; however, a few years after Alfred Marshall systematized economic theories and developed the concept of marginal utility, Léon Walras rebuilt the basis of the classical model, according to marginal analysis, and renamed this theory as neoclassical. During the Great Depression of the 30s, John Maynard Keynes appeared to solve the problems derived by the crisis and he practically built what is now known as macroeconomics. Now the World is back to traditional economics driven by neo-liberalism. Meanwhile and facing the uncontainable advance of capitalism, the social doctrine of the Church has also been in development from the late eighteenth century. Pope Leo XIII wrote the social encyclical Rerum Novarum, and his successors Pius XI, Pius XII, John XXIII, Paul VI, and John Paul II completed the social doctrine, whose principles (dignity of person, solidarity, moral, ethics, responsibility, labor rights, etc.) generally are opposed to mainstream economics, which is represented by a selfish agent maximizing his utility function. Unfortunately, there has not been an open dialogue between the doctrine of the Church and mainstream economic theory; they seem to have taken part in social and economic history without ever interacting. It's time to reach agreements for the sake of humanity.&#x0D;

A dynamic over games drives selfish agents to win-win outcomes.

Understanding human institutions, animal cultures and other social systems requires flexible formalisms that describe how their members change them from within. We introduce a framework for modelling how agents change the games they participate in. We contrast this between-game ‘institutional evolution’ with the more familiar within-game ‘behavioural evolution’. We model institutional change by following small numbers of persistent agents as they select and play a changing series of games. Starting from an initial game, a group of agents trace trajectories through game space by navigating to increasingly preferable games until they converge on ‘attractor’ games. Agents use their ‘institutional preferences' for game features (such as stability, fairness and efficiency) to choose between neighbouring games. We use this framework to pose a pressing question: what kinds of games does institutional evolution select for; what is in the attractors? After computing institutional change trajectories over the two-player space, we find that attractors have disproportionately fair outcomes, even though the agents who produce them are strictly self-interested and indifferent to fairness. This seems to occur because game fairness co-occurs with the self-serving features these agents do actually prefer. We thus present institutional evolution as a mechanism for encouraging the spontaneous emergence of cooperation among small groups of inherently selfish agents, without space, reputation, repetition, or other more familiar mechanisms. Game space trajectories provide a flexible, testable formalism for modelling the interdependencies of behavioural and institutional evolutionary processes, as well as a mechanism for the evolution of cooperation.

Resource Pooling for Shared Fate: Incentivizing Effort in Interdependent Security Games Through Cross-Investments

We consider an interdependent security game with networked agents where each agent chooses an effort/investment level for securing itself. The agents are interdependent in a way where the state of security of one agent depends not only on its own effort but also on that of the other agents, and each agent can benefit from other agents' security investment and effort. Since the agents are interdependent, they try to take advantage of others' security investment and effort and choose to under-invest, which leads to an inefficient Nash equilibrium (NE). While this has been analyzed extensively in the literature, in this study, we take a different angle. Specifically, we consider the possibility of allowing agents to pool their resources, i.e., to both invest in themselves as well as in other agents. We show that the interaction of strategic and selfish agents under resource pooling (RP) improves the agents' effort level as well as their utility compared to a scenario without RP. We show that the social welfare (total utility) at the NE of the game with RP is higher than the maximum social welfare attainable in a game without RP but using an optimal incentive mechanism. Furthermore, we show that while voluntary participation in the latter scenario here, is not generally true, it is guaranteed under RP.

Control of Learning in Anticoordination Network Games

Many games have undesirable Nash equilibria. In such games, a designer's goal is to avoid “bad” equilibria. In this article, we focus on which players to control and how to control them so that the emerging outcome of learning dynamics is desirable. In particular, we consider best-response-type learning dynamics for an anticoordination network game. The designer's goal is to achieve maximum anticoordination with the fewest number of players to control at each round. Our analysis shows that despite the incentive to anticoordinate with neighbors, selfish agents may fail to do so. Accordingly, we relate optimal policies for obtaining maximum anticoordination to the set of Nash equilibria. Noting the combinatorial problem of optimally selecting players in benchmark networks, we develop suboptimal solutions via solving a minimum vertex-cover problem, and by greedily selecting players based on their potential to induce cascading effects. Numerical experiments on random networks show that the cascade-based greedy algorithm can lower the control effort significantly compared to random public advertising policies. Moreover, its control effort is no more than twice the optimal control effort in the worst case.

Budget Feasible Mechanisms on Matroids

Motivated by many practical applications, in this paper we study budget feasible mechanisms with the goal of procuring an independent set of a matroid. More specifically, we are given a matroid {mathcal {M}}=(E,{mathcal {I}}). Each element of the ground set E is controlled by a selfish agent and the cost of the element is private information of the agent itself. A budget limited buyer has additive valuations over the elements of E. The goal is to design an incentive compatible budget feasible mechanism which procures an independent set of the matroid of largest possible value. We also consider the more general case of the pair {mathcal {M}}=(E,{mathcal {I}}) satisfying only the hereditary property. This includes matroids as well as matroid intersection. We show that, given a polynomial time deterministic algorithm that returns an alpha -approximation to the problem of finding a maximum-value independent set in {mathcal {M}}, there exists an individually rational, truthful and budget feasible mechanism which is (3alpha +1)-approximated and runs in polynomial time, thus yielding also a 4-approximation for the special case of matroids.