Strategic Interaction Research Articles

A Dynamic Game of Strategic Carbon Taxation and Energy Pricing with Green Technology Innovation

This paper uses a dynamic game to investigate the strategic interactions between carbon taxation by a coalition of resource consumers and (wellhead) energy pricing by a producers’ cartel under the possibility of innovation in a cheap carbon-free technology. The timing of innovation is uncertain, but can be affected by the amount spent on R &D. The results show that the expectation of possible innovation decreases both the initial carbon tax and producer price, resulting in higher initial resource extraction and carbon emissions. Though this ’green paradox’ effect triggered by possible innovation also will appear in the cooperative case (without strategic interactions), the presence of strategic interactions between resource producers and consumers can somewhat restrain such an effect. For both the resource consumers and a global planner, the optimal R &D to stimulate innovation is an increasing function of the initial CO2 concentration. However, the resource consumers can over-invest in R &D relative to the investment level that a global planner would choose.

Alliance formation between a platform retailer and competing manufacturers in sharing consumer data for product development

This study investigates an alliance formation between competing manufacturers and a monopolistic platform retailer, where consumer data collected by the retailer are available between all the alliance members to utilize for the development of their products. The manufacturers alternatively choose to adopt the direct-to-consumer model, where they collect and utilize own consumer data or sell products without data utilization. They are differentiated from each other in varying degree of substitutability. Under the alliance, the effectiveness of utilizing their rival’s consumer data is proportional to the degree of substitutability. By using a game-theoretic approach, we analyze a strategic interaction between two or three manufacturers and the retailer to derive the condition under which the alliance is successfully formed in equilibrium and to discuss the retailer’s profitability of the alliance formation.We show that participation in the alliance has a direct effect of sharing their rivals’ consumer data and an indirect effect of mitigating price competition between the manufacturers due to the “free-riding” effect of data sharing. Despite these merits, the alliance may not be formed in equilibrium. When the two manufacturers are differentiated to some extent or the three manufacturers are symmetrically differentiated, the alliance is successfully formed. Otherwise, it is never formed in equilibrium. We show that the retailer benefits the most from the alliance with two intermediately differentiated manufacturers and that an increase in the number of manufacturers participating in the alliance does not necessarily improve the retailer’s profitability.

The Persistence of the Emotional Lexicon in Political Discourse

Several researchers argue that political discourse or, especially, political communication, is strategic interaction, because several actors participate in this communication, such as: a politician and the public, or a politician – a politician. Political discourse is one of the most emotionally consistent. The emotional universe in political discourse is sometimes even exaggerated and/or false. It is represented by means such as: affective verbs, conditional-optative verbs, inverted topic, adjectives at the superlative degree. The purpose of political discourse is to manipulate the opinion of the public so that the speaker emerges a winner, to present his own opinion in a reasoned manner. The speaker’s desire is to gain the public’s trust, to accumulate sympathizers, to spread political ideologies in masse, most often political speech arouses controversial opinions among the public, therefore it is considered a speech with a strong degree of emotionality.

The Self-Perceived E-Learning Environment Scale: Development and Psychometric Properties in a Nursing Students Samples.

E-learning is increasingly used in education, creating a learning environment that needs to be studied thoroughly for developing new learning opportunities. Drawing from the community of inquiry framework, the present work provides the theoretical foundation and measurement validation of the Self-Perceived E-Learning Environment Scale (SEES). The SEES measures core aspects of the e-learning context: autonomous strategic planning, social interaction, and teacher feedback. Through two studies carried out in sample of nursing students, an exploratory (n = 302) and confirmatory (n = 245) factor analysis revealed a three-factor structure. The results provide empirical evidence of appropriate psychometric properties (i.e., reliability, validity). The SEES can be used in a higher education context, particularly in nursing education programs. Theoretical, methodological, and practical implications for exploring the e-learning environment in nursing educational settings are discussed.

Voice attractiveness and decision making in third-party punishment.

This study examined the impact of an attractive voice compared to an unattractive voice in an economic game. Results showed that proposers with an attractive voice were perceived as more reasonable in their monetary allocations and were less likely to receive punishment for unfair allocation.

Pengeluaran Pemerintah Kabupaten/Kota di Provinsi Riau: Pendekatan Spasial

Traditional determinants of government spending include income, grants, demographic and political factors. But a government's spending policy can also be influenced by its neighboring governments' spending policies. This study uses Spatial Autoregressive (SAR) spatial econometrics to observe the existence of strategic interactions in District/City Government spending in the Riau Province for the 2011-2019 period. The research results show that there is a strategic interaction in government spending.

Stochastic Dynamic Information Flow Tracking game using supervised learning for detecting advanced persistent threats

Advanced persistent threats (APTs) are organized prolonged cyberattacks by sophisticated attackers with the intent of stealing critical information. Although APT activities are stealthy and evade detection by traditional detection tools, they interact with the system components to make progress in the attack. These interactions lead to information flows that are recorded in the form of a system log. Dynamic Information Flow Tracking (DIFT) has been shown to be an effective way to detect APTs using information flows. A DIFT-based detection mechanism dynamically performs security analysis on the information flows to detect possible attacks. However, wide range security analysis using DIFT results in a significant increase in performance overhead and high rates of false-positives and false-negatives. In this paper, we model the strategic interaction between APT and DIFT as a non-cooperative stochastic game. The game unfolds on a state space constructed from an information flow graph (IFG) that is extracted from the system log. The objective of the APT in the game is to choose transitions in the IFG to find an optimal path in the IFG from an entry point of the attack to an attack target. On the other hand, the objective of DIFT is to dynamically select nodes in the IFG to perform security analysis for detecting APT. Our game model has imperfect information as the players are unaware of the actions of the opponent. We consider two scenarios of the game (i) the false-positive and false-negative rates of DIFT (i.e., transition probabilities of the game) are known and (ii) the false-positive and false-negative rates are unknown. For case (i), we propose a value iteration-based algorithm and prove that the solution converges to the optimal solution (Nash equilibrium). Case (ii) translates to an incomplete information game with unknown transition probabilities. For case (ii), we propose a supervised learning-based algorithm, referred to as Hierarchical Supervised Learning (HSL) algorithm. HSL integrates a neural network, to predict the value vector of the game, with a policy iteration algorithm to compute an approximate equilibrium. We implemented our algorithms for cases (i) and (ii) on real attack datasets for nation state and ransomware attacks and validated the performance of our approach. We compared the performance of the HSL algorithm when the transition probabilities are unknown with instances with known transition probabilities and demonstrated that HSL algorithm converges to a solution close to optimal (i.e., optimal value vector) while the value vector obtained using greedy does not converge to optimal for 44.4% of the states and the mean absolute error is almost 200 times that of the HSL.

Intergroup conflict as contest and disease

Intergroup conflict has been conceptualized as a strategic interaction (conflict-as-contest) and separately as a pathological condition (conflict-as-disease). We highlight how insights and tools from the former perspective can potentially inform the latter. Harnessing the science of strategic decision-making can facilitate the development of novel approaches for mitigating intergroup conflict.

Altruistic preferences in global emission games

This paper analyses the impact of altruism on the individual country goverments’ incentives to reduce global polluting emissions. The game theory perspective provides insights into the strategic decision-making processes of the governments regarding the problem of climate change. We propose a model of strategic interactions among countries in which each government is concerned with its own benefit, as well as with the benefits of all the other countries.The model is a vector-valued non-cooperative game that permits the representation of situations in which the preferences of the governments are incomplete and there is imprecision about the degrees of altruism. The focus is on the identification of the potential equilibria that will eventually be reached when the governments show different attitudes towards other countries or groups of countries. As a result, we show that the incorporation of altruism into the model produces equilibria with a positive effect on the reduction of emissions.

Golden gazes: Gaze direction and emotional context promote prosocial behavior by increasing attributions of empathy and perspective-taking.

Prosocial behavior is fundamental to societies. But when and toward whom do humans act generously? We investigate the impact of a listeners' gaze direction and the emotional context of the story heard on (a) perceptions of their social cognition skills and (b) prosocial decisions toward them. In three experiments (two preregistered, N = 486), human participants witnessed prerecorded video encounters between a listener (visible) and a speaker (audible, not visible). The listener either established eye contact, averted gaze, or showed a mixed gaze pattern (gaze direction), while the speaker told a neutral or negatively valenced autobiographic episode (emotional context). Participants rated the listeners' empathy and perspective-taking after each video and played the trust game (Study 1) or the dictator game (Study 2) with the listener. Replicating previous findings, occasional gaze avoidance, especially during negative narrations, increased attributions of social understanding to the listener. Critically, mediation analyses revealed that listeners perceived as empathic and taking perspective were ultimately treated with more trust and generosity in strategic and nonstrategic economic games, suggesting that social signals and contextual cues can serve as an indication of another's reputation, thereby promoting indirect reciprocity. Last, in Study 3, we show that emotional context, but not listeners' gaze behavior, promoted the spread of generosity toward anonymous, previously unobserved individuals in a dictator game, driven by social cognition skills attributed to the listener. We conclude that social signals and contextual cues can be important drivers of cooperation in societies via mechanisms such as indirect reciprocity and social contagion of generosity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

How Advocacy Nonprofits Interact With and Impact Business: Introducing a Strategic Confrontation and Collaboration Interaction Model (SCCIM)

This paper proposes a distinctive strategic model (Strategic Confrontational and Collaborative Interaction Model [SCCIM]) for nonprofit organizations acting within the business ecosystem. The SCCIM maps both confrontational and collaborative strategies and tactics, thus accommodating the extensive range of nonprofit interaction alternatives toward business. Whereas confrontational and collaborative methods are well researched in the nonprofit-political realm, a comprehensive overview of these nonprofit strategies in the economic sphere is currently lacking. This research builds on both the business management and social movement literature, extending existing approaches via case analysis with a nonprofit-centric perspective, leveraging stakeholder theory. The resulting encompassing model provides a theoretical framework that may generate alternative insights for further academic research in nonprofit–business interaction. In addition, the SCCIM may be leveraged as a tool for practitioners: first, to enhance nonprofits’ strategies and tactics toward business, and second, to optimize the impact of the chosen interactions.

A risk-averse day-ahead bidding strategy of transactive energy sharing microgrids with data-driven chance constraints

The rapid development of microgrids (MGs) with various prosumers promotes the accommodation of renewable distributed generation (RDG) and provides platforms for local energy sharing among prosumers. However, the operational uncertainties pose enormous challenges to the day-ahead bidding of MGs in the wholesale electricity market and there is an urgent need for a local market to facilitate the local energy sharing. Thus, this paper proposes a risk-averse day-ahead bidding strategy for MGs with full consideration of the multiple uncertainties originating from the wholesale electricity market, RDG and loads. Based on the transactive energy (TE) sharing concept, the local market is formulated as a Stackelberg game (SG) to effectively capture the strategic interaction among the MG and prosumers, where a distributed iterative algorithm with a bisection approach that only requires exchanging TE-related information is adopted to achieve the SG equilibrium without compromising privacy concerns. To handle the uncertainties of RDG and loads, the power balances are formulated as chance constraints and a data-driven quantile forecasting method is developed for achieving the computational tractability of chance constraints without any prior knowledge or probability distribution assumptions. Furthermore, a risk criterion of the conditional value-at-risk is incorporated in the day-ahead bidding model of MGs for risk aversion towards uncertainties of the wholesale electricity market. The effectiveness of the proposed solution is extensively demonstrated through numerical simulation.

The impact of political competition on low-carbon technology innovation: From the perspective of officials’ personal characteristics

Political competition is reflected in the strategic interaction of regional government's behavioral motivation driven by “official tournament.” Especially since environmental protection was officially included in the official performance appraisal system in 2005, the interactive trend of local governments’ economic development and environmental protection is more obvious. The policy uncertainty caused by this interaction is a major challenge for the government to promote regional sustainable development. Different from previous studies that only focus on the impact of political competition on environmental governance, this paper studies the impact of political competition on the low-carbon technology innovation from the perspective of sustainable development. Combined with the characteristics of Provincial Party Secretaries and Provincial Governors, the influence mechanism of political competition on low-carbon technology innovation is analyzed based on the method of spatial dubin model. Firstly, it finds that political competition has a significant negative impact on low-carbon technology innovation in the region and its adjacent regions, which is mainly caused by the political competition of Provincial Governors, while the political competition of Provincial Party Secretaries has no significant impact on low-carbon technology innovation. Secondly, the results of the mediation effect test show that reducing the intensity of environmental regulation in exchange for economic growth and political promotion opportunities is one of the reasons why the political competition of Provincial Governors inhibits low-carbon technology innovation. Thirdly, the results of the heterogeneity test of official characteristics show that the age of Provincial Governors is less and the tenure is longer, the stronger the inhibiting effect of Provincial Governors’ political competition on low-carbon technology innovation is. These conclusions provide policy suggestions for enterprises to make environmental investment decisions under different political environments.

On the transmission of guilt aversion and the evolution of trust

This paper studies the evolution of trust and trustworthiness by modeling the intergenerational transmission of guilt aversion. The results depend both on features of strategic interaction and on parental transmission. We show that if there is complete information of opponents' traits, independent of parenting style, the share of high-guilt agents in society weakly increases over time, and trust and trustworthiness are maximized. Moreover, when traits are not observable, different levels of guilt always coexist, and trust and trustworthiness might also increase when parents have imperfect empathy in the transmission of traits or if there is homophily in society.

Evading the algorithm: increased propensity for tax evasion and norm violations in human-computer interactions

Today's modern world is characterized by an increasing shift from human-to-human interaction toward human-computer-interaction (HCI). With the implementation of artificial agents as inspectors, as can be seen in today's airports, supermarkets, or, most recently, within the context of the COVID-19 pandemic, our everyday life is progressively shaped around interacting with automated agents. While our understanding of HCI is evolving, it is still in nascent stages. This is particularly true in the sphere of non-cooperative strategic interactions between humans and automated agents, which remains largely unexplored and calls for further investigation. A deeper understanding of the factors influencing strategic decision-making processes within HCI situations, and how perceptions of automated agents' capabilities might influence these decisions, is required. This gap is addressed by extending a non-cooperative inspection-game experiment with a tax-evasion frame, implementing automated agents as inspectors. Here, a within-subject design is used to investigate (1) how HCI differs from human-to-human interactions in this context and (2) how the complexity and perceived capabilities of automated agents affect human decision-making. The results indicate significant differences in decisions to evade taxes, with participants more likely to evade taxes when they are inspected by automated agents rather than by humans. These results may also be transferred to norm violations more generally, which may become more likely when participants are controlled by computers rather than by humans. Our results further show that participants were less likely to evade taxes when playing against an automated agent described as a complex AI, compared to an automated agent described as a simple algorithm, once they had experienced different agents.

Do Small Players Undermine Cooperation in Asymmetric Games?

Do Small Players Undermine Cooperation in Asymmetric Games?

Pursuit Problem of Unmanned Aerial Vehicles

The study examines scenarios involving a single pursuer tracking a single evader, as well as situations where multiple pursuers are involved in chasing multiple evaders. We formulate this problem as a search and pursuit problem for unmanned aerial vehicles (UAVs). Game theory offers a mathematical framework to model and examine strategic interactions involving multiple decision-makers. By employing game theory principles to address the search and pursuit problem, our objective is to optimize the efficiency of strategies for detecting and capturing unmanned aerial vehicles (UAVs).

Severity and deservedness determine signalled trustworthiness in third party punishment.

Studies on third-party punishment (TPP) have shown it promotes cooperation and prosocial behaviour, albeit at a cost to the punisher. Contrary to the view that such punishment is entirely altruistic, recent research suggests that punishers gain reputational benefits from third-party punishing in the form of increased trustworthiness. Nevertheless, both how the signal is determined and the honesty of the signalling function of TPP have not been fully examined. Here we present the results of four experiments (n = 1695, prolific.co) in which we examined how TPP signalling varies as a function of its deservedness and severity. Experiments 1A and 1B use incentivized economic game paradigms to show how deservedness, impacts the trustworthiness signalled by the punisher. Experiment 2A expands on traditional dichotomous punishment decisions to show how signalled trustworthiness depends on different levels of TPP severity. Experiment 2B isolates the signalling effectiveness of severity by decoupling it from the corresponding incurred cost to punish and examines its impact on signalled trustworthiness. Overall, we found that punishment signalling is sensitive to deservedness and severity but not independent of other factors such as the cost to punish.

Managing ecological thresholds of a risky commons.

Common resources are often overexploited and appear subject to critical transitions from one stable state to another antagonistic state. Many times resulting in tragedy of the commons (TOC)-exploitation of shared resources for personal gain/payoffs, leading to worse outcomes or extinction. An adequate response would be strategic interaction, such as inspection and punishment by institutions to avoid TOC. This strategic interaction is often coupled with dynamically changing common resources. However, effect of strategic interaction in complex, coupled socio-ecological systems is less studied. Here, we develop replicator equations using evolving games in which strategy and common resources co-evolve. We consider the shared commons as fish dynamics governed by the intrinsic growth rate, predation and harvesting. The joint dynamics exhibit an oscillatory TOC, revealing that institutions need to pay special attention to intrinsic growth rate and nonlinear interaction. Our research shows that the co-evolving system exhibits a broader range of dynamics when predation is present compared to the disengaged fishery system. We conclude that the usefulness, chances and challenges of modelling co-evolutionary games to create sustainable systems merit further research.

The psychology of negative-sum competition in strategic interactions.

Many real-life examples-from interpersonal rivalries to international conflicts-suggest that people actively engage in competitive behavior even when it is negative sum (benefiting the self at a greater cost to others). This often leads to loss spirals where everyone-including the winner-ends up losing. Our research seeks to understand the psychology of such negative-sum competition in a controlled setting. To do so, we introduce an experimental paradigm in which paired participants have the option to repeatedly perform a behavior that causes a relatively small gain for the self and a larger loss to the other. Although they have the freedom not to engage in the behavior, most participants actively do so and incur substantial losses. We propose that an important reason behind the phenomena is shallow thinking-focusing on the immediate benefit to the self while overlooking the downstream consequences of how the behavior will influence their counterparts' actions. In support of the proposition, we find that participants are less likely to engage in negative-sum behavior, if they are advised to consider the downstream consequences of their actions, or if they are put in a less frenzied decision environment, which facilitates deeper thinking (acting in discrete vs. continuous time). We discuss how our results differ from prior findings and the implications of our research for mitigating negative-sum competition and loss spirals in real life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).