Selfish Strategy Research Articles

SIM: Achieving High Profit through Integration of Selfish Strategy into Innocent Mining

SIM: Achieving High Profit through Integration of Selfish Strategy into Innocent Mining

Social dilemma in foraging behavior and evolution of cooperation by learning

We consider foraging behaviors in a two-dimensional continuum space and show that a cooperative chasing strategy can emerge in a social dilemma. Predators can use two different chasing strategies: A direct chasing strategy (DCS) and a group chasing strategy (GCS). The DCS is a selfish strategy with which a chaser moves straight toward the nearest prey, and the GCS is a cooperative strategy in the sense that the chaser chooses the chasing direction for the group at a cost of its own speed. A prey flees away from the nearest hazard, either a chaser or the boundary, within its recognition range. We check the capturing activities of each strategy and find a social dilemma between the two strategies because the GCS is more efficient for the group whereas the DCS is better individually. Using a series of numerical simulations, we further show that the cooperative strategy can proliferate when a learning process of nearby successful strategies is introduced.

Stigmatization towards healthcare personnel during the first COVID-19 wave in Central and Northern Mexico.

The evidence all over the world shows an alarming increase in the stigmatization of health personnel during the COVID-19 pandemic. We sought to explore possible psychological factors that help explain the disposition to stigmatize health personnel in the central and northern regions of Mexico. Two studies explore possible psychological factors to explain the disposition to stigmatize healthcare personnel (HP) in Mexico during the COVID-19 pandemic. In study one, 520 participants responded to three instruments that measure the disposition to stigmatize, the perceived contagion risk, and the positive beliefs towards HP. Results showed a generalized low disposition to stigmatization, where only a small percentage obtained high scores. A regression analysis identified that stigmatization towards HP can derive mainly from the perception of risk of contagion, although positive beliefs of HP decrease this disposition. The second study extends this finding by analyzing responses of 286 participants to seven instruments measuring factors hypothesized as predictors towards stigmatization: uncertainty generated by the pandemic, selfish strategies to face off the pandemic, social capital, trust in institutions, perceived vulnerability of contagion, perceived risk of contagion, and positive beliefs towards HP. A path analysis reveals that the main predictor of stigmatization is the perceived risk of contagion, increased by the strategy of selfishness, and the uncertainty generated by the pandemic. These results are discussed emphasizing the importance of cooperation and community ties to prevent the stigmatization of HP in the context of sanitary emergencies generated by contagious diseases.

Best Reply Player Against Mixed Evolutionarily Stable Strategy User

We consider matrix games with two phenotypes (players): one following a mixed evolutionarily stable strategy and another one that always plays a best reply against the action played by its opponent in the previous round (best reply player, BR). We focus on iterated games and well-mixed games with repetition (that is, the mean number of repetitions is positive, but not infinite). In both interaction schemes, there are conditions on the payoff matrix guaranteeing that the best reply player can replace the mixed ESS player. This is possible because best reply players in pairs, individually following their own selfish strategies, develop cycles where the bigger payoff can compensate their disadvantage compared with the ESS players. Well-mixed interaction is one of the basic assumptions of classical evolutionary matrix game theory. However, if the players repeat the game with certain probability, then they can react to their opponents’ behavior. Our main result is that the classical mixed ESS loses its general stability in the well-mixed population games with repetition in the sense that it can happen to be overrun by the BR player.

Flux variability analysis reveals a tragedy of commons in cancer cells

In metabolism processes, enzymes are involved in a cooperative manner of step by step mechanisms trying to guarantee cell survival. Besides, every change in a biological system and its environment within a cell could lead to metabolism alterations due to natural cell response. So, it is reasonable to find metabolism variations within a cell in different conditions. Such enzymes behaviors are very similar to a competitive game where different players try to play for reaching a specific outcome, either by a cooperative or selfish strategy. In this study, we have explored an assumption in which there exists a shift in playing strategy of enzymes from normal to a different biological condition (here cancer) by investigation of pattern alterations in the metabolic subsystem of four cancer cell types (in four cancer stages) using flux calculations and Gene Ontology analysis. Based on the results, we found some metabolic subsystems which contained less flux variation during the cancer progression. The results indicate there might be a game-theoretic strategy in which changing from a beneficial (cooperative) to a selfish strategy happened during cancer stages. It also could be interesting since picking a similar game strategy for all cells would eventually lead the system toward a tragedy of commons (i.e., lower overall fitness for a group or population), in the case of cancer cells, cause the system to be finally disrupted, means death.

A further study of some Markovian Bitcoin models from Göbel et al.

We consider two different continuous-time Markov chain models recently studied in Göbel et al.[8], which were created to model the interactions between a small pool of miners, and a larger collection of miners, within the Bitcoin network. The first model we discuss represents the case where all miners behave honestly and follow the Bitcoin protocol, while the second model represents the case where the smaller pool of miners use the Selfish Mining strategy of Eyal and Sirer[3]. We give a new derivation of the stationary distribution of the process in the honest mining case and further build on the results of Göbel et al.[8] by showing that the normalizing constant can be expressed in closed-form. We also use similar techniques to derive expressions for the Laplace transforms of the transition functions. We then illustrate how these techniques yield similar expressions for the stationary distribution of the process when the smaller pool implements Selfish Mining: the Laplace transforms of the transition functions can be calculated as well. Lastly, we briefly explain how our methods can be extended to more general models of a similar type.

Recommender systems with selfish users

Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task of protecting user privacy by applying a “soft filter” on personal data provided to the system. In this work, we pose the question: “how should the software agent control the user feedback submitted to a recommender system in a way that is most privacy preserving, while the user still enjoys most of the benefits of the recommender system?”. We consider a set of such agents, each of which aims to protect the privacy of its serving user by submitting to the recommender system server a version of her real rating profile. The fact that issued recommendations to a user depend on the collective rating profiles by all agents gives rise to a novel game-theoretic setup that unveils the trade-off between privacy preservation of each user and the quality of recommendation they receive. Privacy is quantified through a distance metric between declared and an “initial” random rating profile; the latter is assumed to provide a “neutral” starting point for the disclosure of the real profile. We allow different users to have different perception of their privacy through a user-dependent utility function of this distance. The quality of recommendations for each user depends on submitted ratings of all users, including the ratings of the user to whom the recommendation is provided. We prove the existence of a Nash equilibrium point (NEP), and we derive conditions for that. We show that user strategies converge to the NEP after an iterative best-response strategy update sequence that involves circulation of aggregate quantities in the system and no revelation of real ratings. We also present various modes of user cooperation in rating declaration, by which users mutually benefit in terms of privacy. We evaluate and compare cooperative and selfish strategies in their performance in terms of privacy preservation and recommendation quality through real movie datasets.

Altruistic Control of Connected Automated Vehicles in Mixed-Autonomy Multi-Lane Highway Traffic

We consider the problem of altruistic control of connected automated vehicles (CAVs) on mixed-autonomy multi-lane highways to mitigate moving traffic jams resulting from car-following dynamics of human-driven vehicles (HDVs). In most of the existing studies on CAVs in multi-lane settings, vehicle controller design philosophy is based on a selfish driving strategy that exclusively addresses the ego vehicle objectives. To improve overall traffic smoothness, we propose an altruistic control strategy for CAVs that aims to maximize the driving comfort and traffic efficiency of both the ego vehicle and surrounding HDVs. We formulate the problem of altruistic control under a model predictive control (MPC) framework to optimize acceleration and lane change sequences of CAVs. In order to efficiently solve the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, we decompose it into three non-convex subproblems, each of which can be transformed into a convex quadratic program via penalty based reformulation of the optimal velocity with relative velocity (OVRV) car-following model. Simulation results demonstrate significant improvements in traffic flow via altruistic CAV actions over selfish strategies on both single- and multi-lane roads.

Collaborative Spatial Reuse in wireless networks via selfish Multi-Armed Bandits

Next-generation wireless deployments are characterized by being dense and uncoordinated, which often leads to inefficient use of resources and poor performance. To solve this, we envision the utilization of completely decentralized mechanisms to enable Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work, we study the exploration-exploitation trade-off by means of the ε-greedy, EXP3, UCB and Thompson sampling action-selection, and compare their performance. In addition, we study the implications of selecting actions simultaneously in an adversarial setting (i.e., concurrently), and compare it with a sequential approach. Our results show that optimal proportional fairness can be achieved, even when no information about neighboring networks is available to the learners and Wireless Networks (WNs) operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, especially for ε-greedy and EXP3. These strategies, contrary to UCB and Thompson sampling, base their operation on the absolute experienced reward, rather than on its distribution. We identify the cause of this variability to be the adversarial setting of our setup in which the set of most played actions provide intermittent good/poor performance depending on the neighboring decisions. We also show that learning sequentially, even if using a selfish strategy, contributes to minimize this variability. The sequential approach is therefore shown to effectively deal with the challenges posed by the adversarial settings that are typically found in decentralized WNs.

Resource-sharing among autonomous agents

We study a scenario for cloud services based on autonomous resource management agents in situations of competition for limited resources. In the scenario, autonomous agents make independent decisions on resource consumption in a competitive environment. Altruistic and selfish strategies for agent behaviour are simulated and compared with respect to whether they lead to successful resource management in the overall system, and how much information exchange is needed among the agents for the strategies to work. Our results imply that local agent information could be sufficient for global optimisation. Also, the selfish strategy proved stable compared to uninformed altruistic behaviour.

Ecological and demographic correlates of cooperation from individual to budding dispersal.

Identifying the ecological and demographic factors that promote the evolution of cooperation is a major challenge for evolutionary biologists. Explanations for the adaptive evolution of cooperation seek to determine which factors make reproduction in cooperative groups more favourable than independent breeding or other selfish strategies. A vast majority of the hypotheses posit that cooperative groups emerge in the context of philopatry, high costs of dispersal, high population density and environmental stability. This route to cooperation, however, fails to explain a growing body of empirical evidence in which cooperation is not associated with one or more of these predictors. We propose an alternative evolutionary path towards the emergence of cooperation that accounts for the disparities observed in the current literature. We find that when dispersal is mediated by a group mode of dispersal, commonly termed budding dispersal, our mathematical model reveals an association between cooperation and immigration, lower costs of dispersal, low population density and environmental variability. Furthermore, by studying the continuum from the individual to the partial and full budding mode of dispersal, we can explicitly explain why the correlates of cooperation change under budding. This enables us to outline a general model for the evolution of cooperation that accounts for a substantial amount of empirical evidence. Our results suggest that natural selection may have favoured two major contrasting pathways for the evolution of cooperation depending on a set of key ecological and demographic factors.

Strategy intervention for the evolution of fairness.

The ‘irrational’ preference for fairness has attracted increasing attention. Although previous studies have focused on the effects of spitefulness on the evolution of fairness, they did not consider non-monotonic rejections shown in behavioral experiments. In this paper, we introduce a non-monotonic rejection in an evolutionary model of the Ultimatum Game. We propose strategy intervention to study the evolution of fairness in general structured populations. By sequentially adding five strategies into the competition between a fair strategy and a selfish strategy, we arrive at the following conclusions. First, the evolution of fairness is inhibited by altruism, but it is promoted by spitefulness. Second, the non-monotonic rejection helps fairness overcome selfishness. Particularly for group-structured populations, we analytically investigate how fairness, selfishness, altruism, and spitefulness are affected by population size, mutation, and migration in the competition among seven strategies. Our results may provide important insights into understanding the evolutionary origin of fairness.

The effect of the stake size on the evolution of fairness

The stake-size effect on the evolution of fairness has attracted increasing attention from experiments for a long time, yet there has not been a clear and consistent conclusion. Budgetary restrictions make it necessary to construct theoretical models. Here, we investigate the stake-size effect, which has been neglected by almost all previous models, in an evolutionary model of the Ultimatum Game. Our study proceeds in group-structured populations of finite size. For the Moran process and the Wright-Fisher process, we calculate the precise expressions of the average offer and the average demand under weak selection. The increase of the stake size causes populations to be closer to the rational selfish strategy, and we analyze how three key parameters influence the approach speed. We divide all possible stakes according to the evolution of generosity and tolerance, and find that the majority of all possible stakes promote generosity and tolerance for small group numbers. The largest stake promotes tolerance to an extreme. The intermediate stake or the largest stake promotes generosity to an extreme, and we analyze the effect of three key parameters on it. Our results can be used to help design more reasonable and more efficient experiments under restricted budgets.

Equilibrium with predictive routeing in the online version of the Braess paradox

The agents of a distributed adaptive system perceive the current state of their environment and make decisions which action to perform. The actions are both reactive and proactive. Reactivity can be supported by the availability of real-time data and proactivity can be supported by anticipatory techniques. Recent investigations proved that if the agents use selfish strategy, then in some situations sometimes the system maybe worst off with real-time data than without real-time data, even if anticipatory techniques are applied to predict the future state of the environment. This study investigates that version of the Braess paradox, where each subsequent agent of the flow may select a different route, using real-time data and anticipatory techniques. The authors contribute to the state-of-the-art by proving that the traffic distribution in this Braess paradox approximates the Nash equilibrium.

Collective optimization problems with optimal decentralized selfish strategies

In this paper, we consider cooperative multi-agent systems minimizing a social cost. Cooperation is induced by the cost that penalizes the divergence of each agent from the average collective behavior. In principle, the optimal solution is centralized and requires a complete communication graph. We study this problem for two important system input-output norms, the l1 induced norm and the per-agent H2 norm squared, as function of the number of agents, n, and various cost structures. For the case of identical agents, and the simplest cost setting, we show that the optimal social solution is always the optimal decentralized selfish solution. For more general cost functions, we show that the optimal solution is always decentralized in the l1 induced norm case. In the case of the per-agent H2 norm squared cost, we show that the optimal decentralized selfish solution is socially optimal in the limit of large n. All of these also hold for several classes of problems with nonidentical agents. In simple terms, these results, identify important problem classes where decentralized/selfish behaviors are socially optimal, and for which inter-agent communication is or becomes unnecessary for large n.

The Role of Prosocialness and Trust in the Consumption of Water as a Limited Resource

This research analyzes the role of prosocialness and trust in the use of water as a limited resource under situations of competition or cooperation. For this purpose, 107 participants played the role of farmers and made decisions about irrigating their fields in the web-based multiplayer game Irrigania. Before the simulation exercise, participants’ prosocialness and trust levels were evaluated and they were randomly assigned to an experimental condition (competition or cooperation). Repeated measures analysis, using the 10 fields and the experimental conditions as factors, showed that, in the cooperation condition, farmers and their villages used a less selfish strategy to cultivate their fields, which produced greater benefits. Under competition, benefits to farmers and their villages were reduced over time. Mediational analysis shows that the selfish irrigation strategy fully mediated the relationship between prosocialness and accumulated profits; prosocial individuals choose less selfish irrigation strategies and, in turn, accumulated more benefit. Moreover, moderation analysis shows that trust moderated the link between prosocialness and water use strategy by strengthening the negative effect of prosocialness on selection of selfish strategies. The implications of these results highlight the importance of promoting the necessary trust to develop prosocial strategies in collectives; therefore, the efficacy of interventions, such as the creation of cooperative educational contexts or organization of collective actions with groups affected by water scarcity, are discussed.

The Pizzagame: A virtual public goods game to assess cooperative behavior in children and adolescents

Social dilemmas are characterized by conflicts between immediate self-interest and long-term collective goals. Although such conflicts lie at the heart of various challenging social interactions, we know little about how cooperation in these situations develops. To extend work on social dilemmas to child and adolescent samples, we developed an age-appropriate computer task (the Pizzagame) with the structural features of a public goods game (PGG). We administered the Pizzagame to a sample of 191 children 9 to 16years of age. Subjects were led to believe they were playing the game over the Internet with three sets of two same-aged, same-sex co-players. In fact, the co-players were computer-generated and programmed to expose children to three consecutive conditions: (1) a cooperative strategy, (2) a selfish strategy, and (3) divergent cooperative-selfish strategies. Supporting the validity of the Pizzagame, our results revealed that children and adolescents displayed conditional cooperation, such that their contributions rose with the increasing cooperativeness of their co-players. Age and gender did not influence children and adolescents' cooperative behavior within each condition. However, older children adapted their behavior more flexibly between conditions to parallel the strategies of their co-players. These results support the utility of the Pizzagame as a feasible, reliable, and valid instrument for assessing and quantifying child and adolescent cooperative behavior. Moreover, these findings extend previous work showing that age influences cooperative behavior in the PGG.

Ecology and multilevel selection explain aggression in spider colonies

Progress in sociobiology continues to be hindered by abstract debates over methodology and the relative importance of within‐group vs. between‐group selection. We need concrete biological examples to ground discussions in empirical data. Recent work argued that the levels of aggression in social spider colonies are explained by group‐level adaptation. Here, we examine this conclusion using models that incorporate ecological detail while remaining consistent with kin‐ and multilevel selection frameworks. We show that although levels of aggression are driven, in part, by between‐group selection, incorporating universal within‐group competition provides a striking fit to the data that is inconsistent with pure group‐level adaptation. Instead, our analyses suggest that aggression is favoured primarily as a selfish strategy to compete for resources, despite causing lower group foraging efficiency or higher risk of group extinction. We argue that sociobiology will benefit from a pluralistic approach and stronger links between ecologically informed models and data.

Love Thy Neighbor: Sharing and Cooperativity in the Gut Microbiota.

To persist in the competitive gastrointestinal ecosystem, microbes often enforce selfish strategies that limit resource loss to neighboring bacteria. In contrast, a recent study in Nature by Rakoff-Nahoum etal. (2016) reveals that one commensal bacterium releases nutrients to benefit another species, which reciprocally provides growth-promoting factors to the producer.

Network centric versus user centric multihoming strategies in LTE/WiFi networks

In this paper, we consider the interworking between multiple wireless access networks and study the multihomed users’ performance for different fourth-generation (4G)/WiFi multihoming techniques. We specifically compare two points of view for multihoming: network centric—wherein the scheduler is controlled by the network—versus user centric—where each user chooses a splitting policy for its packets. In the former, we study two proportional fairness strategies: a global one applied to the whole network and a local one applied to each wireless access network apart. In the user-centric approach, we study user policies based on the information received from the network and consider two variants: a simple one in which the user has only information on the peak rates of the radio interfaces and an optimized selfish policy in which the user receives complete information on the interfaces peak rates and traffic intensities. We then prove that this optimal selfish strategy achieves a global optimum for the system. Our numerical results show that network-centric strategies are better than user-centric ones in terms of achievable throughput but are worse in terms of computational complexity.