Evolutionary Game-theoretic Model Research Articles

Coordination Mechanisms for Traceable Products in the Context of Live Streaming E-Commerce from the Perspective of Platform, Anchor, and Consumer

The meteoric rise of live streaming e-commerce, an emergent form of electronic commerce, is accompanied by frequent occurrences of “live streaming mishaps”. Live streaming’s visual nature offers a more varied and intuitive method for showcasing traceable products, thereby enhancing customer engagement and contracting customers. However, due to the diverging interests, conflicts and contradictions among the stakeholders in traceable products persist, introducing challenges and complexities to its management. We develop an evolutionary game-theoretical model involving three key entities: the platform, anchors (live stream hosts), and consumers. Through the establishment of a payment matrix, we analyze their interactive behaviors and equilibrium states. Additionally, we examine the impacts of key factors on the system and find that platforms and anchors play a pivotal “dual-core” role in traceable product management within the live streaming e-commerce landscape. Moreover, factors such as market attention, anchor characteristics, and consumer dissatisfaction emerge as critical determinants for effective traceable product management. This research contributes valuable insights into enhancing coordination of traceable products in live streaming e-commerce. It offers perspectives on the evolutionary dynamics and strategies for selecting traceable products by platforms, broadcasters, and consumers.

Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes

While autonomous artificial agents are assumed to perfectly execute the strategies they are programmed with, humans who design them may make mistakes. These mistakes may lead to a misalignment between the humans’ intended goals and their agents’ observed behavior, a problem of value alignment. Such an alignment problem may have particularly strong consequences when these autonomous systems are used in social contexts that involve some form of collective risk. By means of an evolutionary game theoretical model, we investigate whether errors in the configuration of artificial agents change the outcome of a collective-risk dilemma, in comparison to a scenario with no delegation. Delegation is here distinguished from no-delegation simply by the moment at which a mistake occurs: either when programming/choosing the agent (in case of delegation) or when executing the actions at each round of the game (in case of no-delegation). We find that, while errors decrease success rate, it is better to delegate and commit to a somewhat flawed strategy, perfectly executed by an autonomous agent, than to commit execution errors directly. Our model also shows that in the long-term, delegation strategies should be favored over no-delegation, if given the choice.

Indirect reciprocity with Bayesian reasoning and biases.

Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation.

Explaining the evolution of gossip

Gossip, the exchange of personal information about absent third parties, is ubiquitous in human societies. However, the evolution of gossip remains a puzzle. The current article proposes an evolutionary cycle of gossip and uses an agent-based evolutionary game-theoretic model to assess it. We argue that the evolution of gossip is the joint consequence of its reputation dissemination and selfishness deterrence functions. Specifically, the dissemination of information about individuals' reputations leads more individuals to condition their behavior on others' reputations. This induces individuals to behave more cooperatively toward gossipers in order to improve their reputations. As a result, gossiping has an evolutionary advantage that leads to its proliferation. The evolution of gossip further facilitates these two functions of gossip and sustains the evolutionary cycle.

Impact of sustained lockdown during Covid-19 pandemic on behavioural dynamics through evolutionary game theoretic model

Impact of sustained lockdown during Covid-19 pandemic on behavioural dynamics through evolutionary game theoretic model

Hostility prevents the tragedy of the commons in metapopulation with asymmetric migration: Alessonfromqueenlessants.

A colony of the queenless ant species, Pristomyrmex punctatus, can broadly be seen as consisting of small-body sized worker ants and relatively larger body-sized cheater ants. Hence, in the presence of intercolony migration, a set of constituent colonies act as a metapopulation exclusively composed of cooperators and defectors. Such a setup facilitates an evolutionary game-theoretic replication-selection model of population dynamics of the ants in a metapopulation. Using the model, we analytically probe the effects of territoriality induced hostility. Such hostility in the ant metapopulation proves to be crucial in preventing the tragedy of the commons, specifically, the workforce, a social good formed by cooperation. This mechanism applies to any metapopulation-not necessarily the ants-composed of cooperators and defectors where interpopulation migration occurs asymmetrically, i.e., cooperators and defectors migrate at different rates. Furthermore, our model validates that there is evolutionary benefit behind the queenless ants' behavior of showing more hostility towards the immigrants from nearby colonies than those from the far-off ones. In order to calibrate our model's parameters, we have extensively used the data available on the queenless ant species, P. punctatus.

Exploration on the reasons for low efficiency of arable land protection policy in China: an evolutionary game theoretic model

Exploration on the reasons for low efficiency of arable land protection policy in China: an evolutionary game theoretic model

Consideration of hotspots in the selection of supervision schemes to reduce illegal dumping of construction and demolition waste.

Illegal dumping of construction and demolition waste continues to be a problem hindering circular economy efforts. Controlling illegal dumping necessitates establishing an effective surveillance scheme and investigating the proper penalty value. This problem has previously been described as a game theory problem, with the government and construction contractors as the players. Taking into account illegal dumping hotspots, which can often be identified through topographical and geographical characteristics, is also important when establishing supervision strategies. This study develops an evolutionary game-theoretic model that can assist in devising effective supervision strategies to control the spread of illegal dumping, while taking into consideration hotspots. In specific, this study investigates the suitability of two alternative strategies: police patrols and hybrid monitoring through both patrols and the installation of closed-circuit television cameras in hotspots. The model was applied to two case studies, using parameters informed by real-world contexts, to demonstrate its potential for selecting suitable strategies according to local situations. The results suggest that nine possible scenarios govern the stable evolutionary strategies of game players, with five scenarios in which contractors converge to adopt legal dumping. Accordingly, based on the parameters of the region (hotspot areas, cost of supervision, efficiency of patrols, penalties, etc.), governments would be able to assess which of the strategies would lead to long-term compliance of contractors while also increasing their payoffs. Further analysis allowed to determine the minimum efficiency required, and simulations were performed to demonstrate the influence of different supervision efficiencies and penalties on the evolutionary strategies.

Emergence of Punishment in Social Dilemma with Environmental Feedback

Altruistic punishment (or punishment) has been extensively shown as an important mechanism for promoting cooperation in human societies. In AI, the emergence of punishment has received much recent interest. In this paper, we contribute with a novel evolutionary game theoretic model to study the impacts of environmental feedback. Whereas a population of agents plays public goods games, there exists a third-party population whose payoffs depend not only on whether to punish or not, but also on the state of the environment (e.g., how cooperative the agents in a social dilemma are). Focusing on one-shot public goods games, we show that environmental feedback, by itself, can lead to the emergence of punishment. We analyze the co-evolution of punishment and cooperation, and derive conditions for their co-presence, co-dominance and co-extinction. Moreover, we show that the system can exhibit bistability as well as cyclic dynamics. Our findings provide a new explanation for the emergence of punishment. On the other hand, our results also alert the need for careful design of implementing punishment in multi-agent systems, as the resulting evolutionary dynamics can be somewhat complex.

Gaps in modelling animal migration with evolutionary game theory: infection can favour the loss of migration.

Ongoing environmental changes alter how natural selection shapes animal migration. Understanding how these changes play out theoretically can be done using evolutionary game theoretic (EGT) approaches, such as looking for evolutionarily stable strategies. Here, we first describe historical patterns of how EGT models have explored different drivers of migration. We find that there are substantial gaps in both the taxa (mammals, amphibians, reptiles, insects) and mechanisms (mutualism, interspecific competition) included in past EGT models of migration. Although enemy interactions, including parasites, are increasingly considered in models of animal migration, they remain the least studied of factors for migration considered to date. Furthermore, few papers look at changes in migration in response to perturbations (e.g. climate change, new species interactions). To address this gap, we present a new EGT model to understand how infection with a novel parasite changes host migration. We find three possible outcomes when migrants encounter novel parasites: maintenance of migration (despite the added infection cost), loss of migration (evolutionary shift to residency) or population collapse, depending on the risk and cost of getting infected, and the cost currency. Our work demonstrates how emerging infection can alter animal behaviour such as migration. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Biomarkers or biotargets? Using competition to lure cancer cells into evolutionary traps.

Cancer biomarkers provide information on the characteristics and extent of cancer progression and help inform clinical decision-making. However, they can also play functional roles in oncogenesis, from enabling metastases and inducing angiogenesis to promoting resistance to chemotherapy. The resulting evolution could bias estimates of cancer progression and lead to suboptimal treatment decisions. We create an evolutionary game theoretic model of cell-cell competition among cancer cells with different levels of biomarker production. We design and simulate therapies on top of this pre-existing game and examine population and biomarker dynamics. Using total biomarker as a proxy for population size generally underestimates chemotherapy efficacy and overestimates targeted therapy efficacy. If biomarker production promotes resistance and a targeted therapy against the biomarker exists, this dynamic can be used to set an evolutionary trap. After chemotherapy selects for a high biomarker-producing cancer cell population, targeted therapy could be highly effective for cancer extinction. Rather than using the most effective therapy given the cancer's current biomarker level and population size, it is more effective to 'overshoot' and utilize an evolutionary trap when the aim is extinction. Increasing cell-cell competition, as influenced by biomarker levels, can help prime and set these traps. Evolution of functional biomarkers amplify the limitations of using total biomarker levels as a measure of tumor size when designing therapeutic protocols. Evolutionarily enlightened therapeutic strategies may be highly effective, assuming a targeted therapy against the biomarker is available.

The evolution of partner specificity in mutualisms.

Mutualistic species vary in their level of partner specificity, which has important evolutionary, ecological, and management implications. Yet, the evolutionary mechanisms which underpin partner specificity are not fully understood. Most work on specialization focuses on the trade-off between generalism and specialism, where specialists receive more benefits from preferred partners at the expense of benefits from non-preferred partners, while generalists receive similar benefits from all partners. Because all mutualisms involve some degree of both cooperation and conflict between partners, we highlight that specialization to a mutualistic partner can be cooperative, increasing benefit to a focal species and a partner, or antagonistic, increasing resource extraction by a focal species from a partner. We devise an evolutionary game theoretic model to assess the evolutionary dynamics of cooperative specialization, antagonistic specialization, and generalism. Our model shows that cooperative specialization leads to bistability: stable equilibria with a specialist host and its preferred partner excluding all others. We also show that under cooperative specialization with spatial effects, generalists can thrive at the boundaries between differing specialist patches. Under antagonistic specialization, generalism is evolutionarily stable. We provide predictions for how a cooperation-antagonism continuum may determine the patterns of partner specificity that develop within mutualistic relationships.

The synergy between two threats: Disinformation and COVID-19

The breakdown of trusted sources of information is probably one of the most serious problems today, since in the absence of a common ground, it will be impossible to address the problems that trouble our contemporary world. The COVID-19 pandemic is just a recent situation where the lack of agreed stances has led to failure and hopelessness. In fact, disinformation surrounding the COVID-19 has been a distinctive feature of this pandemic since its very beginning and has hampered what is perhaps the most important initiative to prevent the spread of the coronavirus, viz., an effective communication between scientifically minded health authorities and the general public. To investigate how disinformation threatens epistemic security, here we propose and solve analytically an evolutionary game-theoretic model where the individuals must accurately estimate some property of their hazardous environment. They can either explore the environment or copy the estimate from another individual, who may display a distorted version of its estimate. We find that the exploration-only strategy is optimal when the environment is relatively safe and the individuals are not reliable. In this doomsday scenario, disinformation erodes trust and suppresses the ability of the individuals to share information with one another.

Bargain to Be Dominant.

AbstractThis article proposes an evolutionary game theoretical model in which individuals negotiate to reach a compromise settlement on who takes the dominant role in a dominant-subordinate relationship. The negotiation process is formalized by bargaining theory in economics, which makes our model different from alternative models on the formation of dominant-subordinate relationships based on detailed biological mechanisms. We find that evolution leads to a "cooperative" ("selfish") behavior when an individual takes the dominant (subordinate) role. For example, consider two Polistes wasps in a nest-building pair who try to determine the role assignment of the queen and the subordinate cofoundress. After the roles are assigned, the queen chooses between either sharing or monopolizing reproduction, and the subordinate chooses between exerting either a high or a low effort in rearing or foraging activities. According to our model's prediction, the queen's behavior (sharing reproduction) increases the collective fitness of the colony but potentially hurts her reproductive interest, while the subordinate's behavior (exerting a low effort) decreases the productivity of the colony but helps her to save energy. Such a behavior pair is evolutionarily stable because it guarantees an individual to have the comparative advantage in taking the dominant role against any other individuals via negotiation. This result may shed new light on understanding the formation of dominant-subordinate relationships. In addition, agnostic of detailed biological mechanisms, our mathematical formulation of dominant-subordinate relationships might also have wide applications in animal behavior and beyond, as its empirical support is found in article wasps and humans.

Experts vs. policymakers in the COVID-19 policy response

We build an evolutionary game-theoretic model of the interaction between policymakers and experts in shaping the policy response to the COVID-19 pandemic. Players’ decisions concern two alternative strategies of pandemic management: a “hard” approach, enforcing potentially unpopular measures such as strict confinement orders, and a “soft” approach, based upon voluntary and short-lived social distancing. Policymakers’ decisions may also rely upon expert advice. Unlike experts, policymakers are sensitive to a public consensus incentive that makes lifting restrictions as soon as possible especially desirable. This incentive may conflict with the overall goal of mitigating the effects of the pandemic, leading to a typical policy dilemma. We show that the selection of strategies may be path-dependent, as their initial distribution is a crucial driver of players’ choices. Contingent on cultural factors and the epidemiological conditions, steady states in which both types of players unanimously endorse the strict strategy can coexist with others where experts and policymakers agree on the soft strategy, depending on the initial conditions. The model can also lead to attractive asymmetric equilibria where experts and policymakers endorse different strategies, or to cyclical dynamics where the shares of adoption of strategies oscillate indefinitely around a mixed strategy equilibrium. This multiplicity of equilibria can explain the coexistence of contrasting pandemic countermeasures observed across countries in the first wave of the outbreak. Our results suggest that cross-country differences in the COVID-19 policy response need not be the effect of poor decision making. Instead, they can endogenously result from the interplay between policymakers and experts incentives under the local social, cultural and epidemiological conditions.

Influence of committed volunteers on helping behavior in emergency evacuations

We study how the presence of committed volunteers influences the collective helping behavior in emergency evacuation scenarios. In this study, committed volunteers do not change their decision to help injured persons, implying that other evacuees may adapt their helping behavior through strategic interactions. An evolutionary game theoretic model is developed which is then coupled to a pedestrian movement model to examine the collective helping behavior in evacuations. By systematically controlling the number of committed volunteers and payoff parameters, we have characterized and summarized various collective helping behaviors in phase diagrams. From our numerical simulations, we observe that the existence of committed volunteers can promote cooperation but adding additional committed volunteers is effective only above a minimum number of committed volunteers. This study also highlights that the evolution of collective helping behavior is strongly affected by the evacuation process.

How social learning affects customer behavior under the implementation of TOU in the electricity retailing market

As a representative motivative scheme in demand response implementation, time-of-use (TOU) pricing has significant potentials in achieving load-shifting goals. Meanwhile, social learning among customers, becoming popular with the development of information technologies, also profoundly affects customer consumption decisions. Still, its impacts on power customer consumption behaviors under implementing the TOU scheme remain unexplored. We propose an evolutionary game-theoretical model that incorporated social learning to illustrate how customer power consumption behaviors evolve if they learn from each other, where consuming relations between power consumers and retailers are formulated on account of the bipartite graphs. In every period, the retailers establish the TOU scheme based on the historical demand to optimize its current profits and load-shifting effects, and the customers, in turn, dispatch their consumptions for utility maximizations subjected to the proposed prices. We simulate the different scenarios of consumer behavior decisions verify our model by empirical data. Our results demonstrate how social learning changes consumer behavior and maximizes all consumers' utility. Noteworthy, Social learning brings volatility to the market, which incurs fluctuations for both firms' payoffs and customer utilities. Besides, we derive that the rigid price caps can guarantee customers positive utilities, and more communications among customers will lead to the decay of the entire retailing profits. This research provides decision support for consumer behavior and pricing of power retailing companies in the context of social learning.

Stimuli strategy and learning dynamics promote the wisdom of crowds.

Collective wisdom is the ability of a group to perform more effectively than any individual alone. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that reinforcement learning stimulus may play the role in enhancing collective voting accuracy. And collective voting bias can be dismissed through self-reinforcing global cooperative learning. Numeric simulations suggest that the provided method can increase collective voting accuracy. We conclude that real-world systems might seek reward-based incentive mechanism as an alternative to surmount group decision error.Graphical

Stable federated fog formation: An evolutionary game theoretical approach

Instability within fog federations is considered as a serious problem that degrades the performance of the provided services. The latter may affect the service availability due to fog providers withdrawing their resources. It may either lead to failures for some users invocations, or to an increase in the number of tasks inside the servers’ processing-queue. Such a critical problem strips the fog paradigm from its main characteristic, the low latency factor. As far as we are aware, no work in the literature has addressed the problem of encountering unstable fog federations. Their main concerns were increasing the providers’ payoff regardless of their behavior. To address the aforementioned limitation, this work studies the stability of the federations through modeling the problem as an evolutionary game-theoretical model. Moreover, it devises a decentralized algorithm that implants the Replicator Dynamics model within which expresses the evolutionary dynamics. Experiments are conducted using EUA Datasets to simulate our algorithm and to show that it leads to an evolutionarily stable strategy over time, which stabilizes the federations and improves the Quality-of-Service for the users.

Rewards and penalties in an evolutionary game theoretic model of international environmental agreements

The UNFCC on climate change specifies that all nations must follow the rule of ‘common’ with differentiation regarding their responsibilities for the protection of the global environmental system. Recently, the formulation and stability of the IEA have been increased in the literature by applying the concept of game theory to make the climate agreements successful at the national and the international level. This study provides a novel evolutionary game theoretic model of self-enforcing IEA to overcome the free rider problem. The fundamental difference between our paper and existing literature is that we examine enforcement within a model as IEA has a governing authority while the typical model of enforcement involves a government enforcing a rule that it has imposed. For this purpose, we assign countries into different grades according to their pollution levels, consider a combination of rewards and penalties, use replicator dynamics to derive the conditions for the population steady state, and examine how the proposed regulatory mechanism fares in this steady state. This framework enables us to avoid the free rider and renegotiation problems as well as the rationality assumption. We establish the condition for evolutionary stability. The global environmental problem is managed effectively as a reward-punishment scheme and the monitoring frequency of IEA fulfills this condition. Our results provide an allocation principal with stable conditions under which countries get more benefits by monitoring the IEA and stability of the grand coalition holds.