To reveal the interaction and influence mechanism between emergency rescue entities, and to explore and optimize a cooperation mechanism of emergency rescue entities, a tripartite evolutionary game model of emergency rescue cooperation based on government rescue teams, social emergency organizations, and government support institutions was constructed. The stability of each game subject’s strategy choice was explored. Simulation analysis was applied to investigate the influence mechanism of key parameters on the evolution of the game subject’s strategy combination. The research results show that government rescue teams, social emergency organizations, and government support institutions have consistent political demands and rescue targets in emergency rescue cooperation. The game subjects are driving forces for each other to choose positive strategies. The game evolution process of the emergency cooperation model shows a “mobilization-coordination” feature. At the same time, the emergency capital stock formed based on trust relationships, information matching, and institutional norms between game subjects can promote the evolution of the game system toward (1,1,1). In addition, for government organizations with limited emergency resources, the average allocation of emergency resources is not the optimal solution for emergency rescue efficiency. However, it is easier to achieve the overall target of emergency rescue cooperation by investing limited emergency resources in key variables that match the on-site situation. On this basis, combined with the practice of emergency rescues in emergencies, countermeasures and solutions are proposed to optimize the mechanism and improve the efficiency of emergency rescue cooperation.

Abstract Political preferences are the result of a complex process rooted in specific psychological traits and the environmental context in which an individual operates. Scientific research provides ample evidence that these preferences are largely shaped by mechanisms that served adaptive functions throughout the evolutionary history of our species. The article focuses on the analysis of studies devoted to the evolutionary origins of political views in the domains of conservatism and liberalism. Among evolutionary interpretations, five main sources are most frequently identified as responsible for shaping these preferences: pathogen avoidance, sexual strategies, life history theory, inclusive fitness, and coalitional defense. However, many of these interpretations are limited to specific situations, and some present inconsistent conclusions. Therefore, this article proposes a unification of these concepts. In light of the research discussed, coalitional theories are considered the most coherent framework, as they encompass many different interpretations. The article also proposes a synthesis of these concepts and offers directions for future research.

Artificial Intelligence (AI) systems are increasingly applied to complex tasks that involve interaction with multiple agents. Such interaction-based systems can lead to safety risks. Due to limited perception and prior knowledge, agents acting in the real world may unconsciously hold false beliefs and strategies about their environment, leading to safety risks in their future decisions. For humans, we can usually rely on the high-level theory of mind (ToM) capability to perceive the mental states of others, identify risk-inducing errors, and offer our timely help to keep others away from dangerous situations. Inspired by the biological information processing mechanism of ToM, we propose a brain-inspired theory of mind spiking neural network (ToM-SNN) model to enable agents to perceive such risk-inducing errors inside others' mental states and make decisions to help others when necessary. The ToM-SNN model incorporates the multiple brain areas coordination mechanisms and biologically realistic spiking neural networks (SNNs) trained with Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP). To verify the effectiveness of the ToM-SNN model, we conducted various experiments in the gridworld environments with random agents' starting positions and random blocking walls. Experimental results demonstrate that the agent with the ToM-SNN model selects rescue behavior to help others avoid safety risks based on self-experience and prior knowledge. To the best of our knowledge, thi

In an experimental choice situation, we identify risk-acceptability thresholds and show how such thresholds are updated in response to benchmark information, a recurrent feature of health, safety, and environmental (HS&E) risk governance. We present a theoretical framework linking the observed behavior to an underlying evolutionary parameter, which in this case is (an abstract notion of) risk aversion. The theoretical model allows to predict how the experimental subjects adjust their risk aversion when informed about the risky choices of others. Applications of the framework arise naturally in HS&E settings, where individuals and organizations revise risk thresholds by observing peers, experienced coworkers, or acknowledged experts. By distinguishing confidence-driven inertia from trust-driven overreaction, the paper provides actionable guidance for HS&E risk communication and adaptive risk management.