Spending by the UK’s National Health Service (NHS) on independent healthcare treatment has been increased in recent years and is predicted to sustain its upward trend with the forecast of population growth. Some have viewed this increase as an attempt not to expand the patients’ choices but to privatize public healthcare. This debate poses a social dilemma whether the NHS should stop cooperating with Private providers. This paper contributes to healthcare economic modelling by investigating the evolution of cooperation among three proposed populations: Public Healthcare Providers, Private Healthcare Providers and Patients . The Patient population is included as a main player in the decision-making process by expanding patient’s choices of treatment. We develop a generic basic model that measures the cost of healthcare provision based on given parameters, such as NHS and private healthcare providers’ cost of investments in both sectors, cost of treatments and gained benefits. A patient’s costly punishment is introduced as a mechanism to enhance cooperation among the three populations. Our findings show that cooperation can be improved with the introduction of punishment (patient’s punishment) against defecting providers. Although punishment increases cooperation, it is very costly considering the small improvement in cooperation in comparison to the basic model.

We study the evolution of fairness in a multiplayer version of the classical Ultimatum Game in which a group of N Proposers offers a division of resources to M Responders. In general, the proposal is rejected if the (average) proposed offer is lower than the (average) response threshold in the Responders group. A motivation for our work is the exchange of flexibilities between smart energy communities, where the surplus of one community can be offered to meet the demand of a second community. In the absence of any Responder selection criteria, the co-evolving populations of Proposers and Responders converge to a state in which proposals and acceptance thresholds are low, implying an unfair exchange that favors Proposers. To circumvent this, we test different rules which determine how Responders should be selected, contingent on their declared acceptance thresholds. We find that selecting moderate Responders optimizes overall fairness. Selecting the lowest-demanding Responders maintains unfairness, while selecting the highest-demanding individuals yields a worse outcome for all due to frequent rejected proposals. These results provide a practical message for institutional design and the proposed model allows testing policies and emergent behaviors on the intersection between social choice theory, group bargaining, competition, and fairness elicitation.

Change is inevitable in this fast-moving world. As the environment and people’s needs continuously change, so must the project. In our previous work, we developed an agent-based model of human collaboration that incorporates individual personalities. In this work, we applied a genetic algorithm to select the optimal personality combinations of a team in order to cope with different types of project change. We studied change in the context of three types of tasks: disjunctive (team performance is the performance achieved by the best performing individual), conjunctive (team performance is the performance achieved by the worst performing individual), and additive (team performance is the total performance of the group). Results reveal that different compositions of team personalities are suitable for different dynamic problems and task types. In particular, optimal personalities found for static problems differ from optimal personalities found for dynamic problems.

Self-organized and distributed control methods are increasingly important as they allow multi-agent systems to scale more readily than centralized control techniques. Furthermore, these methods increase system robustness and flexibility. In the online multi-object k-coverage domain studied here, a collective of autonomous agents must dynamically form sub-teams to accomplish two concurrent tasks: target discovery and coverage. Once a target is discovered, the collective of agents must create a sub-team of k-agents to cover the target. The work presented here introduces a novel, entropy-based task selection technique that incorporates signal suppression behaviors found in bee colonies. We test the technique in the online multi-object k-coverage domain while exploring three team properties: heterogeneity, team size, and sensor ranges, and their impact on multi-task accomplishment. Results show that signal suppression helps avoid over-provisioning of team resources to individual targets, dynamically creating sub-teams that simultaneously accomplish target discovery and coverage tasks.

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

The repeated prisoner’s dilemma (RPD) game has revealed how cooperation and competition arise among competitive players in long-run relationships. In the RPD game with no errors, zero-determinant (ZD) strategies allow a player to unilaterally set a linear relationship between the player’s own payoff and the opponent’s payoff regardless of the strategy of the opponent. On the other hand, unconditional strategies such as ALLD and ALLC also unilaterally set a linear relationship. However, little is known about the existence of such strategies in the RPD game with errors. Here, we analytically search for the strategies that enforce a linear payoff relationship under observation error in the RPD game. As a result, we found that, even in the case with observation errors, the only strategy sets that enforce a linear payoff relationship are either ZD strategies or unconditional strategies.

At large scales, typologies of urban form and corresponding generating processes remain an open question with important implications regarding urban planning policies and sustainability. We propose in this paper to generate urban configurations at large scales, typically of districts, with morphogenesis models, and compare these to real configurations according to morphological indicators. Real values are computed on a large sample of districts taken in European urban areas. We calibrate each model and show their complementarity to approach the variety of real urban configurations, paving the way to multi-model approaches of urban morphogenesis.

Human social hierarchy has the unique characteristic of existing in two forms. Firstly, as an informal hierarchy where leaders and followers are implicitly defined by their personal characteristics, and secondly, as an institutional hierarchy where leaders and followers are explicitly appointed by group decision. Although both forms can reduce the time spent in organising collective tasks, institutional hierarchy imposes additional costs. It is therefore natural to question why it emerges at all. The key difference lies in the fact that institutions can create hierarchy with only a single leader, which is unlikely to occur in unregulated informal hierarchy. To investigate if this difference can affect group decision-making and explain the evolution of institutional hierarchy, we first build an opinion-formation model that simulates group decision making. We show that in comparison to informal hierarchy, a single-leader hierarchy reduces (i) the time a group spends to reach consensus, (ii) the variation in consensus time, and (iii) the rate of increase in consensus time as group size increases. We then use this model to simulate the cost of organising a collective action which produces resources, and integrate this into an evolutionary model where individuals can choose between informal or institutional hierarchy. Our results demonstrate that groups evolve preferences towards institutional hierarchy, despite the cost of creating an institution, as it provides a greater organisational advantage which is less affected by group size and inequality.