Evolutionary Game Theory Model Research Articles

The emergence of cooperation in the context of prior agreement with threshold and posterior compensation

Previous experience suggests that prior agreement (a mechanism to test how strongly peers are committed to a common goal) and posterior compensation (a way to compensate cooperators by punishing defectors who violate their commitments) together contribute to the flourishing of cooperation. Far less is known, however, how the determination of the optimal threshold for prior agreement in force and the posterior compensation affect collaboration, and the relevance of different strategies. Here, in the context of public goods games and using an evolutionary game theory model, we investigate the evolution of cooperation and the relevance of different strategies by introducing prior agreements and posterior compensation in structured populations. Analytical results show that posterior compensation does not always promote cooperation and that there are sudden increases and decreases in the first-order phase transition. Interestingly, prior agreements without posterior compensation can also effectively restrain defectors, with cooperation abundance remaining at a high level. Furthermore, the threshold of effective agreement has nonlinear effects on cooperation, with cooperation abundance peaking at intermediate thresholds under specific parameters.

Adaptations to infer fitness interdependence promote the evolution of cooperation.

The evolution of cooperation is a major question in the biological and behavioral sciences. While most theoretical studies model cooperation in the context of an isolated interaction (e.g., a Prisoner's Dilemma), humans live in heterogeneous social environments, characterized by large variations in fitness interdependence-the extent to which one's fitness is affected by others. Theoretical and experimental work indicates that humans can infer, and respond to, variations in interdependence. In a heterogeneous ancestral environment, these psychological mechanisms to infer fitness interdependence could have provided a selective advantage, allowing individuals to maximize their fitness by deciding when and with whom to cooperate. Yet, to date, the link between cognitive inference, variation in fitness interdependence, and cooperation remains unclear. Here we introduce a theoretical framework to study the evolution of inference and cooperation in heterogeneous social environments, where individuals experience interactions with varying levels of corresponding interests. Using a combination of evolutionary game theory and agent-based modeling, we model the evolution of adaptive agents, who incur a cost to infer interdependence, in populations of fixed-behavior agents who always cooperate or defect. Our results indicate that natural selection could promote the evolution of psychological mechanisms to infer fitness interdependence, provided that there is enough variation in fitness interdependence to offset the cost of inference. Under certain conditions, the fixation of adaptive agents results in higher levels of cooperation. This depends crucially on the type of inference performed and the features of the interdependence landscape.

Behavioral Microfoundations of New Practice Adoption: The Effects of Rewards, Training and Population Dynamics

Organizations face challenges when trying to effectively introduce new operational practices that substitute for existing ones. We study how the dynamics due to social comparisons between employees give rise to individual strategic considerations and eventually shape the organizational adoption outcome. We develop an evolutionary game theory model that accounts for these microlevel individual adoption decisions and their impact on macrolevel population adoption equilibria. Social comparisons invoke dynamics that expand the possible outcomes beyond the traditional nonadoption versus full-adoption dichotomy. Specifically, ahead-seeking social comparisons drive the long-term coexistence of practices because employees seek to differentiate their choices from those of others. Meanwhile, behind-averse comparisons create a bandwagon effect that determines adoption depending on the initial fraction of adopters—that is, employees who are trained upfront. These dynamics are robust to various settings: different conceptualizations of social comparisons, each employee responding to more than one kind of social comparison, and nonhomogeneous social comparisons across employees. Moreover, they are material to organizations that seek to maximize their profit when introducing a new practice, by setting the levels of upfront training and adoption rewards. Our results call for senior managers to account for such behavioral traits when managing the introduction of new practices. Profitable adoption critically relies upon matching rewards and training to the type of social comparison. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.00305 .

A model based on the evolutionary game theory for implementing green mining principles in riverine sand and gravel resources

An evolutionary Game Theory (GT) model was presented to study the dynamics between the mine management and government for implementing Green Mining (GM) principles in riverine sand and gravel mines. In this regard, a game was planned in which the mine management is the executing agent, while the government acts as the supervisor. Investigating the performance of the proposed model, regulatory factors such as governmental incentives and penalties were introduced. In order to compare the results, the effects of regulatory measures on strategy changes by mine management were analyzed into two distinct scenarios: classical GT and evolutionary GT, which provide valuable insights into the complex interactions between mine management and government. The Shahed sand and gravel mine near the Kordan River in Alborz province, Iran, was selected as a case study to validate the developed GT models. The results obtained from the classical GT model showed that a fine of $ 0.2 per tonne and a 30% governmental incentive for the mining and equipment costs, may encourage the mine management to choose the GM strategy. Conversely, the results of the evolutionary GT model indicated that by increasing the government subsidy from 30% to 50%, the rate of encouraging the mine management to choose the GM strategy could be accelerated. This situation is similar to the government incentive for mine management in case of fines from $0.2 to $1 per tonne. According to the results of the evolutionary GT model, when the population ratio increases over time, the selected strategy is more compatible and powerful compared to other ones. Therefore, it is concluded that the mine management in Shahed mine may turn to the GM strategy by imposing a penalty more than $0.6 per tonne.

A tripartite evolutionary game analysis of providing subsidies for pick-up/drop-off strategy in carpooling problem

Although the pick-up/drop-off (PUDO) strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking, there is a noticeable hesitancy among commuters to adopt this travel method, despite its numerous benefits. Here, this paper establishes a tripartite evolutionary game theory (EGT) model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system. The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications. Subsequently, an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios. The findings suggest that: firstly, providing subsidies to passengers and drivers, along with deductions for drivers through carpooling platforms, is an effective way to promote wider adoption of the PUDO strategy. Then, the decision-making process is divided into three stages: initial stage, middle stage, and mature stage. PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and, in the mature stage, both passengers and drivers tend to adopt it under carpooling platform subsidies; the factors influencing the costs of waiting and walking times, as well as the subsidies granted to passengers, are essential determinants that require careful consideration by passengers, drivers, and carpooling platforms when choosing the PUDO strategy. Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive.

Applications of evolutionary game theory in urban road transport network: A state of the art review

A sustainable transport infrastructure is one of the pillars of a sustainable city. However, the literature indicates that urbanization, population growth, changes in population density, and motorization make it difficult for the current road transport system to meet mobility needs for a sustainable city. Traffic crashes and congestion on roads are common as a result of increasing travel times, fuel consumption, and carbon emissions, thereby reducing efficiency and sustainability of mobility systems. Managing these issues involves the interaction of multiple decision-makers, such as vehicles, pedestrians, traffic system operators, and authorities. Accordingly, these are well-suited to being analyzed under the guise of game theory. While classical game theory possesses multiple limitations, it can be argued that evolutionary game theory (EGT) models are more effective for real-world scenarios. This manuscript presents a state-of-the-art review on EGT applied to the road transportation network. The manuscript has divided the application of EGT in advancing the transportation network into multiple categories, i.e., choice-based analysis, traffic management, behavioral interactions, routing operation, and transport safety. This manuscript provides an in-depth analysis and a comparative criticism of the various proposed evolutionary game models. Finally, the manuscript discusses the challenges and provides recommendations for future research on evolutionary game models in transportation networks. These insights aim to facilitate targeted activities based on current research needs.

A tripartite evolutionary game model for the hydrogen fuel cell vehicle industry development under government regulation in China

The development of the hydrogen fuel cell vehicle (HFCV) industry constitutes a necessary advancement in the transformation and upgrading of the automotive industry as a whole and is also an important strategic route to help China achieve the objectives of carbon neutrality. How to effectively promote the production of HFCV enterprises and the purchase of HFCVs by consumers has become a critical problem that needs to be solved. Taking into consideration the limited rationality of the government, enterprises and consumers, we applied a tripartite evolutionary game theory (EGT) model to analyze the impact of government regulation on the development of the HFCV industry. The simulation results show that: (1) medium-intensity supervision can promote robust and regulated HFCV industry development; (2) the higher the penalty, the more likely enterprises are to transition to HFCV production; (3) the larger the research and development (R&D) subsidy, the more beneficial it is for enterprises to overcome technical obstacles; (4) higher investment in infrastructure stimulates consumers to purchase HFCVs. Based on these results, some recommendations have provided to support the sustainable development of HFCV industry in China.

Half a century of evolutionary games: a synthesis of theory, application and future directions.

Half a century of evolutionary games: a synthesis of theory, application and future directions.

Cooperation and coordination in heterogeneous populations.

One landmark application of evolutionary game theory is the study of social dilemmas. This literature explores why people cooperate even when there are strong incentives to defect. Much of this literature, however, assumes that interactions are symmetric. Individuals are assumed to have the same strategic options and the same potential pay-offs. Yet many interesting questions arise once individuals are allowed to differ. Here, we study asymmetry in simple coordination games. In our set-up, human participants need to decide how much of their endowment to contribute to a public good. If a group's collective contribution reaches a pre-defined threshold, all group members receive a reward. To account for possible asymmetries, individuals either differ in their endowments or their productivities. According to a theoretical equilibrium analysis, such games tend to have many possible solutions. In equilibrium, group members may contribute the same amount, different amounts or nothing at all. According to our behavioural experiment, however, humans favour the equilibrium in which everyone contributes the same proportion of their endowment. We use these experimental results to highlight the non-trivial effects of inequality on cooperation, and we discuss to which extent models of evolutionary game theory can account for these effects. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Evolutionary game theory to predict the population growth in Few districts of Tamil Nadu and Kerala

The conventional models in Game Theory focuses entirely on the relative fitness of the population agents leaving the dynamic behaviour of the size of population left uncovered. Therefore, a robust model is required to combine both the growth dynamics and internal evolution of agents. Evolutionary Game Theory to Predict the Population Growth in Few Districts of Tamil Nadu and Kerala. In this paper, we develop an Evolutionary Game Theory (EGT) model which is integrated with the Residual deterministic learning (RDL) model to increase the rate of prediction of the population growth. Various deterministic models including Arithmetic Increase, Geometric Increase, Incremental Increase and Simple graphical models are used to improve the prediction ability of EGT. The EGT combined with RDL forms REGT model that is found scalable to predict the long-term population growth in near proximal districts of Tamil Nadu and Kerala. Various inputs affecting the population growth are considered as the inputs to the REGT model to the Matlab simulator. The experimentation shows that the proposed REGT model is better in predicting the population growth than the existing game theory models.

Distributed Service Provisioning for Disaggregated 6G Network Infrastructures

6G Systems are expected to support a variety of services over a common infrastructure that is efficiently shared through slicing. Novel Quality of Experience (QoE) architectural models and resource assignment schemes are needed that are able a) to differentiate Service Data Flows (SDFs) originating from the same or multiple user equipment (UE), b) react to changes of the underlying physical infrastructure and c) scale with the number of connected devices. Currently, this functionality is provided by centralized management and network orchestration platforms suffering scalability issues. Thus, future systems are expected to operate in a distributed manner allowing applications to directly intervene in the relevant control processes to guarantee the required QoE. In response to this, the present study focuses on the development of a novel flow assignment scheme supporting applications running at the UEs. The scheme is based on Evolutionary Game Theory (EGT) and allows the application functions (AFs) responsible for the control of the UE applications to take traffic routing and steering decisions in a fully distributed way minimizing charging costs and maximizing their QoE. The charging function of the EGT model relies on profiling data extracted from an open-source 5G platform deployed in a practically implemented testbed environment.

Diversity enables the jump towards cooperation for the Traveler’s Dilemma

Social dilemmas are situations in which collective welfare is at odds with individual gain. One widely studied example, due to the conflict it poses between human behaviour and game theoretic reasoning, is the Traveler’s Dilemma. The dilemma relies on the players’ incentive to undercut their opponent at the expense of losing a collective high payoff. Such individual incentive leads players to a systematic mutual undercutting until the lowest possible payoff is reached, which is the game’s unique Nash equilibrium. However, if players were satisfied with a high payoff -that is not necessarily higher than their opponent’s- they would both be better off individually and collectively. Here, we explain how it is possible to converge to this cooperative high payoff equilibrium. Our analysis focuses on decomposing the dilemma into a local and a global game. We show that players need to escape the local maximisation and jump to the global game, in order to reach the cooperative equilibrium. Using a dynamic approach, based on evolutionary game theory and learning theory models, we find that diversity, understood as the presence of suboptimal strategies, is the general mechanism that enables the jump towards cooperation.

The territorial nature of aggression in biofilms.

Microbial conflicts have a particularly aggressive nature. In addition to other chemical, mechanical, and biological weapons in their repertoire, bacteria have evolved bacteriocins, which are narrow-spectrum toxins that kill closely related strains. Bacterial cells are known to frequently use their arsenal while competing against each other for nutrients and space. This stands in contrast with the animal world, where conflicts over resources and mating opportunities are far less lethal, and get commonly resolved via ritualized fighting or “limited war” tactics. Prevalence of aggression in microbial communities is usually explained as due to their limited ability to resolve conflicts via signaling as well as their limited ability to pull out from conflicts due to the sessile nature of their life within biofilms. We use an approach that combines Evolutionary Game Theory (EGT) and Individual-based Modeling (IbM) to investigate the origins of aggression in microbial conflicts. In order to understand how the spatial mode of growth affects the cost of a fight, we compare the growth dynamics emerging from engaging in aggression in a well-mixed system to a spatially structured system. To this end, a mathematical model is constructed for the competition between two bacterial strains where each strain produces a diffusible toxin to which the other strain is sensitive. It is observed that in the biofilm growth mode, starting from a mixed layer of two strains, mutual aggression gives rise to an exceedingly high level of spatial segregation, which in turn reduces the cost of aggression on both strains compared to when the same competition occurs in a well-mixed culture. Another observation is that the transition from a mixed layer to segregated growth is characterized by a switch in the overall growth dynamics. An increased “lag time” is observed in the overall population growth curve that is associated with the earlier stages of growth, when each strain is still experiencing the inhibiting effect of the toxin produced by its competitor. Afterwards, an exponential phase of growth kicks in once the competing strains start segregating from each other. The emerging “lag time” arises from the spiteful interactions between the two strains rather than acclimation of cells' internal physiology. Our analysis highlights the territorial nature of microbial conflicts as the key driver to their elevated levels of aggression as it increases the benefit-to-cost ratio of participating in antagonistic interactions.

Sustainable Organic Farming, Food Safety and Pest Management: An Evolutionary Game Analysis

There is an increasing realization that industrial, large-scale agriculture can negatively impact both food quality and the environment, and that alternatives should be thoroughly considered. Consisting of various participants with distinct and often competing interests, organic food chains have a dynamic structure. We consider an evolutionary game theory model for the dynamics of an organic supply chain with farmers, their customers and the government as the main stakeholders. After describing stakeholder strategies and constructing appropriate payoff matrices for the interactions between farmers and customers and between farmers and the government, respectively, sufficient conditions for the stability of the equilibria for the associated replicator equations were found. Those conditions were then interpreted in practical terms, the corresponding possible outcomes being determined and numerically illustrated. It was seen that a sustainable shift from a conventional strategy to an organic one requires the efforts of all involved stakeholders. As far as the evolutionary interaction between farmers and customers is concerned, it was seen that the purchasing power and the organic awareness of customers are of the utmost importance for the establishment and diffusion of organic strategies in the supply chain. Furthermore, a situation in which the preferences of farmers and consumers for an organic (or conventional) strategy change periodically may occur. Regarding the evolutionary interaction between farmers and the government, strong support for organic farmers is needed at first, and then the consumption habits and environmental awareness of the consumers can be cultivated. This promotes the establishment, development and enrichment of an organic supply chain which, at a certain point, can persist even without governmental subsidies.

Abstract CT159: A phase 2a evolutionarily informed, mathematically guided adaptive abiraterone trial in metastatic castrate resistant prostate cancer

Abstract Background: To achieve better prostate cancer control and to delay the emergency of treatment resistance, we developed an evolutionary game theory model using Lotka-Volterra equations with three competing prostate cancer "species": T+, Tp, and T-. T+ prostate cancer cells depend on exogenous androgen; Tp cells express CYP17A1, produce and depend on androgen; and T- cells are androgen-independent and abiraterone (abi)-resistant. We applied this model to guide the on and off treatment cycles with abi for metastatic castration resistant prostate cancer (mCRPC). At the first interim analysis with 11 patients, this approach was shown to prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous abi (Nat Commun. 2017). Here we present the updated data of this phase 2a study with a median follow up of 5.8 years. Methods: Men with asymptomatic or minimal symptomatic mCRPC were enrolled after they achieved > 50% PSA reduction with abi as a frontline therapy for mCRPC. The primary objective is feasibility and is measured by the percentage of abi responsive men who remain to be responsive to abi (defined as > 50% decline of the pre Abi PSA) after completing 2 adaptive treatment cycles. The secondary objective is to assess the clinical benefits by comparing the radiographic progression free survival (rPFS) and overall survival (OS) in men undergoing adaptive Abi therapy to a contemporaneous cohort of patients who met trial eligibility but received continuous abi dosing as standard of care (SOC). Computer simulations of each patient’s clinical course estimated critical evolutionary parameters and then tested alternative strategies for individuals in both cohorts. Result: 17 evaluable patients were enrolled at Moffitt Cancer Center between June 2015 and January 2019. 5 of the 17 patients have completed at least 5 adaptive therapy cycles and 4 remain on study treatment without imaging progression for > 52 months at the data cut off on 1/4/2022. The study is closed for enrollment after demonstrating the feasibility of adaptive therapy. 16 SOC patients treated with continuous abi for mCRPC between 2015 and 2018 were identified through retrospective chart reviewed. Clinical features like Gleason score and metastasis burden are similar between the two groups. Evolution-based application of abiraterone significantly improved (p<0.001) median rPFS (30.4 months) and median OS (58.5 months) in the adaptive group compared to the 14.3 months median rPFS and the 31.3 months median OS in the SOC group. Average cumulative dose in the adaptive cohort was 54% of SOC dosing. Conclusions: Adaptive Abi therapy is feasible in men who responded to Abi as a frontline therapy for mCRPC. The updated data with longer follow up demonstrated OS as well as rPFS benefits of our adaptive therapy approach with less drug usage compared to the conventional continuous Abi. Clinical trial information: NCT02415621 Citation Format: Jingsong Zhang, Joel S. Brown, Jessica Cunningham, Evan Adler, Robert Gatenby. A phase 2a evolutionarily informed, mathematically guided adaptive abiraterone trial in metastatic castrate resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT159.

Behavior evolution of supply chain networks under disruption risk — From aspects of time dynamic and spatial feature

Supply chain disruptions are inevitable and may bring about fatal influence to the system. In this article, we examine how up and downstream firms embedded in a supply chain network engage in decision-making under disruption risk. The decision evolution of the supply chain network, treated as a complex adaptive system (CAS), is investigated from both the aspects of time dynamic and spatial feature by employing the Evolutionary Game Theory (EGT) and Cellular Automata (CA) approach, respectively. Supply chain network agents (i.e., up and downstream firms) are supposed to have two optional responses, positive strategy or negative strategy when facing the disruption risk. By observing a series of simulation experiments on the developed EGT model and CA model, we offer some testable propositions regarding the behavioral nature of the agents in the supply chain network. Some managerial insights in managing supply chain disruption risk are presented as well. We find that the spatial feature of the supply chain network plays a vital role in the evolution process which can lead the system to a more satisfactory equilibrium, compared with the evolution process only considering the time dynamic.

Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance

In this paper, we study how an on-demand service platform could improve its performance through managing user conduct. In such a platform, service providers may reject certain platform-proposed service requests, and their responses, in turn, incentivize users to adjust their conduct. We develop an evolutionary game theory model of user conduct and provider responses that shows that the platform could improve user conduct through either setting a low wage for service providers or implementing priority matching. Building upon these results, we further model providers and users joining and leaving the platform by once again utilizing the evolutionary game theory approach. We find that wage setting alone is a blunt instrument to improve platform performance via managing user conduct, whereas supplementing the wage decision with priority matching could overcome its limitations and serve as an effective strategy to further improve platform performance in terms of growth and profitability. This finding suggests that matching prioritization could be an important strategy for managing platforms with user and provider heterogeneities. In addition, our analysis and results also demonstrate the potential of the evolutionary game theory approach for analyzing the impact of pricing and matching decisions on the performance of large markets. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4391 .

Polarization, antipathy, and political activism

AbstractWe present an evolutionary game theory model in which polarization, antipathy, and political activism are simultaneous consequences of the evolution of individuals' ideologies and their attitudes toward other ideologies. We show that the evolutionary process is likely to result in a vicious path with individuals becoming increasingly extreme and polarized on the ideological spectrum and the society ending up with two politically engaged groups sharing no common grounds and strong hatred against each other.

Epidemic proximity and imitation dynamics drive infodemic waves during the COVID-19 pandemic

An infodemic---an outpouring of information, including misleading and also fake news---is accompanying the current pandemic caused by SARS-CoV-2. In the absence of valid therapeutic approaches, behavioral responses may seriously affect the social dynamics of contagion, so the infodemic may cause confusion and disorientation in the public, leading to possible individually and socially harmful choices. This new phenomenon requires specific modeling efforts to better understand the complex intertwining of the epidemic and infodemic components of a pandemic crisis, with a view to building an integrative public health approach. We propose three models, from epidemiology to game theory, as potential candidates for the onset of the infodemics and statistically assess their accuracy in reproducing real infodemic waves observed in a data set of 390 million tweets collected worldwide. Our results show that evolutionary game-theory models are the most suitable ones to reproduce the observed infodemic modulations around the onset of the local epidemic wave. Furthermore, we find that the number of confirmed COVID-19 reported cases in each country and worldwide are driving the modeling dynamics with opposite effects.

Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies

PurposeThe identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens. MethodsWe developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references. FindingsPredictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval. ImplicationsThe results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.