Serious Games: Human-AI Interaction, Evolution, and Coevolution (2505.16388v1)

Abstract: The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of biological entities. EGT could help predict the potential evolutionary equilibrium of humans and AI. The objective of this work was to examine some of the EGT models relevant to human-AI interaction, evolution, and coevolution. Of thirteen EGT models considered, three were examined: the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition. This selection was based on the widespread acceptance and clear relevance of these models to potential human-AI evolutionary dynamics and coevolutionary trajectories. The Hawk-Dove Game predicts balanced mixed-strategy equilibria based on the costs of conflict. It also shows the potential for balanced coevolution rather than dominance. Iterated Prisoner's Dilemma suggests that repeated interaction may lead to cognitive coevolution. It demonstrates how memory and reciprocity can lead to cooperation. The War of Attrition suggests that competition for resources may result in strategic coevolution, asymmetric equilibria, and conventions on sharing resources. Therefore, EGT may provide a suitable framework to understand and predict the human-AI evolutionary dynamic. However, future research could extend beyond EGT and explore additional frameworks, empirical validation methods, and interdisciplinary perspectives. AI is being shaped by human input and is evolving in response to it. So too, neuroplasticity allows the human brain to grow and evolve in response to stimuli. If humans and AI converge in future, what might be the result of human neuroplasticity combined with an ever-evolving AI? Future research should be mindful of the ethical and cognitive implications of human-AI interaction, evolution, and coevolution.

Exploring Evolutionary Game Theory in Human-AI Interaction and Coevolution

This paper titled "Serious Games: Human-AI Interaction, Evolution, and Coevolution" by Nandini Doreswamy and Louise Horstmanshof probes the application of Evolutionary Game Theory (EGT) models in understanding and predicting the dynamics between human and AI entities. By examining renowned EGT models such as the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition, the paper seeks to articulate how these frameworks can be extrapolated to human-AI evolutionary dynamics.

The rationale for employing EGT lies in its ability to transcend traditional Game Theory's assumptions of player rationality, offering a more biologically grounded perspective where interactions are not strictly utility-driven but shaped by evolutionary pressures. This paper establishes EGT as a salient framework for analyzing AI not merely as computational tools but as evolving entities participating in complex strategic interactions with humans.

Methodological and Theoretical Foundation

The paper investigates thirteen EGT models, narrowing its analytical scope to three foundational games recognized for their explanatory power and relevance to human-AI dynamics. This selection foregrounds the capacity of these models to simulate both competitive and cooperative interactions:

1. Hawk-Dove Game: This model, premised on conflict resolution over resources, illustrates how AI and humans might strike a balance between aggressive and cooperative strategies. The equilibrium achieved herein foresees a scenario where neither strategy achieves dominance, fostering a balanced coevolution.
2. Iterated Prisoner's Dilemma: By allowing repeated interactions and strategy adaptation based on history, this model emphasizes cognitive coevolution between humans and AI. It renders insights into how memory, reciprocity, and reward systems can cement cooperative stances through adaptation and iterative learning.
3. War of Attrition: This model provides a framework for understanding resource competition, where withdrawal is dictated by the cumulative costs accrued. The mooted strategic coevolution could manifest in shared conventions or asymmetrical resource distributions, contingent on cost evaluations and endurance thresholds.

Implications and Future Directions

The paper insinuates that these EGT applications underscore a nuanced evolutionary dance between humans and AI, marked by cooperation, competition, and strategic adaptation. These insights could herald profound implications for managing human-AI relationships, emphasizing strategic positioning as both entities navigate their respective evolutionary pathways.

Furthermore, the research opens avenues for future exploration beyond EGT, advocating the integration of empirical validation and interdisciplinarity to enhance the theoretical robustness and practical applicability of the models. It beckons researchers to examine the cognitive and ethical dimensions of human-AI coevolution, accounting for AI's expanding role and its reciprocal influence on human cognitive trajectories.

Conclusion

By capitalizing on the predictive prowess of EGT, the paper provides an intellectual scaffold to ponder potential future interactions where human and AI systems may evolve in tandem rather than in isolation or opposition. It challenges researchers to converge methodologies, extending beyond EGT to encapsulate diverse theoretical and empirical insights, thereby enriching the discourse on human-AI evolutionary dynamics.

In essence, this work invites renewed scrutiny into strategic interactions within the context of evolving AI technologies, presenting EGT not only as a theoretical construct but as a predictive tool capable of steering the future landscape where humans and AI coalesce in complex, adaptive games.