This exploratory study aims at investigating the decision-making processes, strategic behaviors, and cooperation dynamics that emerge among lower secondary school students engaged in a team-based math competition. The study takes place within a simulated setting of the "Kangourou della Matematica" contest, allowing systematic observation of how students manage problem-solving, assume roles, make decisions under pressure, and interact within cooperative learning environments.

The sample consists of 20 school teams (80 students; average age: 12). The methodology includes a strategic reasoning test, a risk propensity scale, and a short-form personality questionnaire (Big Five) administered in the pre-competition phase. During the contest, an observational grid is completed by teachers or researchers. After the activity, each group fills in a self-assessment form, and each student responds to a questionnaire on their perceived role and the strategies used during the competition.

Data analysis combines quantitative and qualitative approaches: descriptive statistics, principal component analysis (PCA), cluster analysis, and thematic coding of open responses. Preliminary results reveal distinct strategic and behavioural profiles. The comparison between theoretical models and observed behaviours highlights both alignments and discrepancies, emphasizing the influence of relational dynamics, emotional factors, and time pressure in shaping students’ strategic decisions.

This study proposes an integrated and replicable framework for observing and analyzing peer-based strategic interactions in educational contexts. It offers valuable insights for designing didactic interventions that foster intentional strategic thinking and promote reflective, collaborative learning practices in schools.

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This paper develops a two-stage Tullock conflict model with two agents and a violence parameter that determines the onset of open conflict. Agents invest costly effort to compete for a valuable prize, awarded probabilistically following the standard Tullock formulation. The winning probability is the ratio of a player’s effort (raised to a positive power) to the sum of both players’ efforts raised to the same power. When this power equals one, the contest reduces to a lottery. In the canonical two-stage rent-seeking model with linear and symmetric effort costs, the Subgame Perfect Nash Equilibrium (SPNE) predicts that each player expends one-fourth of the prize value in case of open conflict, resulting in equal winning probabilities of one-half. We extend this framework by assuming quadratic effort costs. Under symmetry, equilibrium efforts remain positive and winning probabilities still equalize at one-half. However, we identify multiple equilibria depending on the prize value and the violence parameter. In some parametric settings, unilateral positive effort by one player is sufficient to win without triggering violent conflict. We then introduce cost asymmetry, allowing one player to face lower marginal effort costs. Our analysis shows that even an infinitesimal advantage leads the lower-cost agent to exert strictly higher effort, significantly increasing total effort in open conflict. This results in greater overall waste relative to the symmetric benchmark.

The energy transition has become a strategic priority for industrial firms. In response to the climate emergency, rising fossil fuel prices, and increasing regulatory pressures, many industrial groups are investing in the development of cleaner technologies. In this context, cooperation in environmental R&D is emerging as a key lever to share costs, pool risks, and benefit from positive technological spillovers. Environmental policies and economic instruments, such as emission taxes, further strengthen this trend by encouraging firms to innovate toward low-carbon solutions. Industrial partnerships such as BMW–Toyota (hydrogen fuel cells), Tesla–Panasonic (electric battery innovation), and Renault–Nissan–Mitsubishi (electric vehicles) clearly illustrate this growing tendency toward cooperation in environmental R&D.

Our contribution builds on this dynamic by adopting a non-cooperative game-theoretic approach. We develop a three-stage game: the regulator first sets an emission tax, then firms choose their level of R&D investment, either jointly under a cooperative scheme (Joint Lab) or individually, and finally compete à la Cournot in the product market. We analyze how asymmetries in production efficiency affect the equilibrium R&D effort, the optimal tax, and overall welfare. The main result shows that as asymmetry increases, the joint R&D effort declines, which can diminish the environmental and welfare benefits of cooperation. Beyond a critical threshold, the cooperative regime may become less effective than non-cooperation.

We introduce the Guaranteed Utility Equilibrium (GUE), a strategy profile where each player secures a utility guarantee regardless of the other players' strategies, and the sum of these guarantees equals the highest achievable total utility. GUE possesses several desirable properties that the Dominant Strategy Equilibrium (DSE) lacks, such as collusion-proofness and robustness under repeated play. GUE also ensures that all Bayesian Nash Equilibria are utility-equivalent to the GUE profile. Particularly in dynamic games, GUE arises more frequently than DSE. For instance, a GUE exists in every finite two-player zero-sum game. Furthermore, a stronger version of the core result of the No-Trade Theorem is a direct consequence of the ``no-trade'' strategy profile constituting a GUE. Perhaps the only significant property of DSE not shared by GUE is that a GUE profile is not necessarily a DSE. For mechanism design, however, this distinction is a crucial advantage, as it allows for GUE-implementation in problems where a DSE-implementation of efficiency is impossible; the mechanism in \cite{CLRT}, for instance, is a GUE-implementation in a problem of this kind. The concept also extends naturally to an approximate version ($\eps$-GUE), which is particularly useful in transfer-free mechanism design.

The incorporation of digital learning environments in higher education has driven the development of innovative methodological strategies, such as gamification, aimed at computer-assisted learning. The effectiveness of gamification is conditioned by the adaptation of the methodology to the player’s profile, for which Bartle’s taxonomy of player types distinguishes four profiles: Explorer, Killer, Socializer, and Achiever. This quantitative study analyzes both the distribution of player profiles among engineering faculty and their perceptions regarding the most suitable profile for learning. Additionally, sociological and academic factors influencing the selection of these profiles are examined. The sample size is 532 engineering professors, and a descriptive and inferential analysis was conducted on the answers of those who participated in a training course on gamification in digital environments and answered a questionnaire developed by the authors. The principal findings indicate that most professors identify with the Explorer profile, which is also considered the most suitable for learning, although a discrepancy is observed between one’s own profile and the preference for the most effective profile. Furthermore, the predominant profile among professors differs from the one most attributed to students in the literature, which is the Achiever. Significant differences were also identified according to gender, age, teaching experience, and university tenure. Based on these results, it is recommended that universities strengthen faculty training in the design of personalized game-based learning environments, considering the detected sociological and academic differences.

While the free-rider problem has been studied extensively ([Botta 2013, Yamagishi 1986, Botta 2021]), this has also increased the number of theories and questions to be answered around it. One way in which the free-rider problem is modeled is through a Public Goods Game, in which contributions made to a common pool of resources by players are then scaled and distributed equally to all participants.

In this work, we focus on the "optional" variant of the Public Goods Game, where players may opt for a risk-free (yet often lower) reward instead of participating in the game. Building on [Botta 2024] and [Grau 2022], we combine fractional (targeting a subset of free-riders) and incomplete (reducing benefits while imposing fines) punishment via optimal control to enhance cooperation in the entire population. Our mechanism mirrors real-world practices and helps to save the financial amount spent on penalizing just a part of the total number of free riders while
still obtaining the desired cooperation.

To tune the combined policy, we design an appropriate objective function to solve a problem of optimization with restriction, where the restriction is given by the replicator dynamics problem, including the fractional and incomplete punishment.

The findings provide policymakers with actionable insights; while stronger instantaneous punishments increase cooperation (and reduce the overall costs of punishment in the long term), they imply higher initial investments. Even though high cost can be challenging for managers, especially when free-riders make up a large part of the group, the proposed control mechanism offers a cost-effective alternative by strategically targeting only a fraction of free-riders or adjusting the fines, balancing enforcement costs with long-term cooperative outcomes.

Corruption requires a coalition to form and reach an agreement. Is there a cheap way to stop any agreement from being reached? We find an optimal mechanism that resembles Poker. The players' hands are synthetic asymmetric information, and they create a lemons problem in the market for bribes. Our Poker mechanism is robust: it thwarts bribes regardless of the negotiation procedure, including alternating offers bargaining, Dutch auctions, arbitration, and so on. Our mechanism's cost is inversely proportional to the number of players. So when we embed our mechanism in regulatory approval and regulatory compliance settings, we find that it is optimal to hire competing auditors to each case. In compliance cases, there is a trade-off between rewarding the agent for honesty and punishing the agent for non-compliance. This trade-off is resolved by rigging the Poker hand distribution against the agent. Our results are also pertinent to robust mechanism design and worst-case information structures.

We study the extent to which cultural differences in bargaining behavior are context-dependent. In an online experiment, we examine bargaining behavior in the US and Japan using the ultimatum game, questionnaire items, and multiple contextualized scenarios. Our participants were presented with two general questions about negotiation behavior, one about the tendency to negotiate and another about the level of aggressiveness in negotiations. They also played the ultimatum game, making two decisions, one as the proposer and one as the responder. Finally, they responded to questions about 13 different real-life negotiation scenarios, indicating whether they would try to negotiate or not, and in 7 scenarios stating what their proposal to the other party would be. The participants also indicated how frequent each of the negotiation scenarios was in their environment. We found significant cross-country differences in willingness to negotiate in 6 out of the 13 scenarios. In half of these cases, the Americans were more willing to negotiate; in the other half, the Japanese were more willing. We also found significant differences in the level of the proposals to the other party in two out of seven scenarios. Similarly, in one of these cases, the proposals of the Americans were higher, while in the other the Japanese proposed higher amounts. Our analyses show that the differences in negotiation behavior can be partly explained by how frequent the negotiation scenarios are in the participants’ environment. Across the two countries, the questionnaire measure of the general tendency to negotiate highly correlated with the willingness to negotiate in the real-life scenarios. Moreover, our measure of the level of aggressiveness in negotiations significantly correlated with the level of the proposals made in the scenarios. On the contrary, we did not find significant correlations between the ultimatum game behaviors and the responses to the scenarios.

Collusion and corruption in public procurement undermine competition, inflate contract prices, distort the allocation of public resources, and reduce the social value of awarded projects. These practices pose persistent regulatory challenges, especially in institutional contexts where enforcement is imperfect and agent behavior evolves over time.

We propose an evolutionary game model using replicator dynamics, in which three types of agents interact within the procurement process: the contracting authority, the bidders, and the regulatory agency. Each agent has two available strategies and seeks to maximize its payoff, which depends on its own decisions and those of the other agents. In our setup, bidders may engage in collusive behavior, and the contracting authority may act corruptly, capturing real-world scenarios such as bid rigging facilitated by bribery or informal agreements.

Evolutionary game theory provides a dynamic framework to study how strategies like collusion and corruption emerge and persist in populations of boundedly rational agents. Unlike static equilibrium models, it captures adaptive behavior through imitation and selection processes, without assuming full information or perfect foresight. This makes it particularly suitable for analyzing regulatory environments where incentives, sanctions, and behaviors evolve jointly.

The model parameters reflect the benefits, costs, and potential sanctions associated with each strategic combination, representing the institutional trade-offs faced by agents in diverse settings. We conduct a stability analysis of the system, characterize the eight pure-strategy equilibrium points, and explore dynamic trajectories through simulations under various relevant parameter configurations, allowing us to examine the evolution of strategies over time.

The results provide insights into how the interaction between collusion and corruption shapes procurement outcomes and inform the design of regulatory policies aimed at fostering more transparent and efficient environments.

Under the dynamic dual-credit and carbon tax policies, the consumer utility function, the automobile manufacturer profit function, and the evolutionary game model involving the government and automobile manufacturers are constructed, and dual-credit transaction prices are incorporated into the model to analyze the evolutionary stability strategy of automobile manufacturers' production decisions under the dynamic dual-credit policy and carbon tax policy. The results show that 1) the combination of dynamic dual-credit and static carbon tax policies is optimal, and a moderate increase in the carbon tax rate is helpful to improve the enthusiasm of new energy vehicle manufacturers; and 2) when the credit price is around CNY 800, it can best improve the enthusiasm of new energy vehicle manufacturers. Both too-high and too-low prices are not conducive to the market role of the dual-credit policy. Additionally, 3) A higher carbon tax rate is conducive to the rapid development of the new energy vehicle industry in the short term, but it is easy to cause market fluctuations.