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Traditional neuroticism assessments primarily rely on self-report questionnaires, which can be difficult to implement in highly confrontational scenarios and are susceptible to subjective biases. To overcome these limitations, this study develops a machine learning-based approach using behavioral data to predict an opponent's neuroticism in competitive environments. We analyzed behavioral records from 167 participants on the MiaoSuan Wargame platform. After data cleaning and feature selection, key behavioral features associated with neuroticism were identified, and predictive models were developed. Neuroticism was assessed using the 8-item neuroticism subscale of the Big Five Inventory. Results indicate that this method can effectively infer an individual's neuroticism level. The best-performing model was LinearSVR, which balances interpretability, robustness to noise, and the ability to capture moderate nonlinear relationships-making it suitable for behavior-based psychological inference tasks. The correlation between predicted scores and self-reported questionnaire scores was 0.606, the R-squared value was 0.354, and the test-retest reliability was 0.516. These behavioral features provide valuable insights into neuroticism prediction and have practical applications in psychological assessment, particularly in competitive environments where conventional methods are impractical. This study demonstrates the feasibility of behavior-based neuroticism assessment and suggests future research directions, including refining feature selection techniques and expanding the application scenarios.

To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to autonomously classify detected targets into predefined categories with equipment information. To simulate different levels of human guidance, we set up two modes for the model: the easier-to-obtain but low-value-type cues simulated by Mode 1 and the labor-intensive but high-value class labels simulated by Mode 2. In addition, to analyze the respective roles of human experience guidance and machine data learning in target classification tasks, the article proposes a machine-based learner (TCARL_M) with zero human participation and a human-based interventionist with full human guidance (TCARL_H). Finally, based on the simulation data from a wargame, we carried out performance evaluation and application analysis for the proposed models in terms of target prediction and target classification, respectively, and the obtained results demonstrate that TCARL_H-M can not only greatly save labor costs, but achieve more competitive classification accuracy compared with our TCARL_M, TCARL_H, a purely supervised model-long short-term memory network (LSTM), a classic active learning algorithm-Query By Committee (QBC), and the common active learning model-uncertainty sampling (Uncertainty).

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Background. Wargaming has a long history as a tool for understanding the complexity of conflict. Although wargames have shown their relevance across topics and time, the immersive nature of wargames and the guild-like communities that surround them have often resisted the social scientific advances that occurred alongside the evolution of warfare. However, recent work raises new possibilities for integrating wargaming practices and social scientific methods. Purpose. Develop the experimental wargaming method and practice. Prioritizing the focus on iteration, control, and generalizability within experimental design can provide new opportunities for wargames to answer broader questions about decision-making, crisis behaviors, and patterns of outcomes. Method. The International Crisis Wargame developed in 2018 demonstrates the viability of experimental wargaming, and models the process of theorizing, designing, developing, and executing these wargames. It also identifies what makes games more or less experimental and details how experimental design influenced choices in the game. Conclusion. Experimental wargames are a promising new tool for both the social science and the wargaming communities. A proposed new research agenda for experimental design within wargames would support this nascent method

Urban waterlogging seriously threatens the safety of urban residents and properties. Wargame simulation research on resident emergency evacuation from waterlogged areas can determine the effectiveness of emergency response plans for high risk events at low cost. Based on wargame theory and emergency evacuation plans, we used a wargame exercise method, incorporating qualitative and quantitative aspects, to build an urban waterlogging disaster emergency shelter using a wargame exercise and evaluation model. The simulation was empirically tested in Daoli District of Harbin. The results showed that the wargame simulation scored 96.40 points, evaluated as good. From the simulation results, wargame simulation of urban waterlogging emergency procedures for disaster response can improve the flexibility and capacity for command, management and decision-making in emergency management departments.

Abstract Why since 1945 have nuclear weapons not been used? Political scientists have cited five basic reasons: deterrence, practicality, precedent, reputation, and ethics. Scholars attempting to weight these factors face a dearth of empirical data. Declassified records of political-military wargames played by U.S. policymakers, however, open up new avenues for theory testing. An investigation of the willingness of U.S. “strategic elites”—experts with experience in diplomatic or military strategy—to use nuclear weapons in a sample of twenty-six political-military wargames reveals that elite players were reluctant to cross the nuclear threshold against both nuclear-armed and nonnuclear-armed adversaries. The only uses of nuclear weapons in the sample occurred in two wargames with nuclear adversaries. Players’ arguments for restraint in the wargames invoked reputational aversion: decisionmakers feared the opprobrium they would face if they used nuclear weapons. Wargame records also strongly support the power of deterrence and basic practicality—whether conventional weapons could accomplish the same goals—as reasons for widespread hesitation to use nuclear weapons. Precedent and ethical aversions to using nuclear weapons were less common. Finally, players also demonstrated some conformity to what they thought the president expected of them.