First of all, thank you very much for your responses. The theoretical and methodological advancements we have made in extending biform games beyond their traditional boundaries offer a significant contribution to existing strategic decision modeling approaches. Our approach transcends static and outdated formulations by integrating biform structures into a dynamic, AI-augmented decision-making framework capable of modeling real-time transitions between cooperation and competition. Such dynamics are indispensable for capturing the complexity of contemporary strategic interactions.
Our contribution goes beyond a mere refinement; it presents a mathematically rigorous and conceptually coherent framework explicitly designed for robust application under conditions of uncertainty. The geometric formalization of uncertainty sets facilitates both intuitive interpretability and analytical precision, thereby enhancing the framework's theoretical soundness and practical viability. This methodology synthesizes classical, fuzzy, and stochastic paradigms into a unified and flexible modeling architecture. Within this context, some key studies that could serve as the theoretical foundation for our proposed biform game modeling are as follows:
Original Message:
Sent: 07-06-2025 11:39
From: Alexander Kolker
Subject: A New Approach to Strategic Analysis for a Changing World
Bruce, I agree with you. On top of that, my concern was how AI and its role was defined in the discussed context.
So far, I saw only some slogans/buzzwords like "AI enhances this framework....". It was not large language model (LLM), but what was it? In what way it enhances...data-driven learning..
?
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Alexander Kolker
ge healthcare
MILWAUKEE WI
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Original Message:
Sent: 07-06-2025 10:48
From: Bruce Hartman
Subject: A New Approach to Strategic Analysis for a Changing World
Gerhard, I see you mentioned biform games. I thought they were deprecated now. Dror and Summerfield showed they were just a form of stochastic decision making. I was around at the time Nichalin and Moshe wrote the paper. It's interesting, given Alexander's comment.
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Bruce Hartman
Professor
University of St. Francis
Tucson, AZ United States
bruce@ahartman.net
website: https://sites.google.com/ahartman.net/drbrucehartman/Home
blog:http://supplychainandlogistics.org
Original Message:
Sent: 07-05-2025 13:55
From: Gerhard-Wilhelm Weber
Subject: A New Approach to Strategic Analysis for a Changing World
Dear Alexander Kolker,
Classical game theory assumes fully rational agents, a premise often challenged by real-world bounded rationality and behavioral complexity. Despite this, it remains unique in formally modeling complex strategic interdependencies among multiple decision-makers. Sequential dynamic decision-making, by contrast, focuses on optimizing individual agents' decisions over time but lacks comprehensive treatment of multi-agent interactions.
Artificial intelligence enhances this framework by enabling adaptive, data-driven learning that supports real-time adjustment, continual updating, and effective uncertainty management. Methods such as multi-agent reinforcement learning allow game-theoretic models to dynamically incorporate evolving behaviors and incomplete information.
This synergy enables analysis of emergent phenomena including spontaneous cooperation, shifting alliances, and nuanced signaling in uncertain, co-opetitive contexts. While sequential models optimize individual strategies, AI-augmented game theory uniquely captures adaptive, collective dynamics, offering richer insights and more robust decision support for complex strategic environments, such as the incorporation of uncertainty sets-including polyhedral, fuzzy, ellipsoidal, and stochastic models-demonstrated in robust decision-making frameworks:
- Alparslan Gök S.Z., Ergün, S., Kırlar, B. B., Özcan, İ., & Tayman, A. (2023). Fuzzy perspective of online games by using cryptography and cooperative game theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 31(06), 891-915.
- Özmen, A., & Weber, G. W. (2014). RMARS: robustification of multivariate adaptive regression spline under polyhedral uncertainty. Journal of Computational and Applied Mathematics, 259, 914-924.
- Savku, E., & Weber, G. W. (2022). Stochastic differential games for optimal investment problems in a Markov regime-switching jump-diffusion market. Annals of Operations Research, 312(2), 1171-1196.
With warm regards,
Jacek Dominik Śledziński
Faculty of Engineering Management, Poznan University of Technology
📧 jacek.dominic@gmail.com
İsmail Özcan (Contact person)
PhD
📧 ismailozcanmath@gmail.com
Gerhard-Wilhelm Weber
Faculty of Engineering Management, Poznan University of Technology
📧 gerhard.weber@put.poznan.pl
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Gerhard-Wilhelm Weber
Professor
Poznan University of Technology
Poznan
Original Message:
Sent: 07-05-2025 10:43
From: Alexander Kolker
Subject: A New Approach to Strategic Analysis for a Changing World
Re: "traditional tools of strategic analysis long relied upon for this purpose may no longer be sufficient. Their limitations stem primarily from their static nature and inability to leverage the full potential of modern information technologies, especially AI."
A methodology for sequential decision-making in OR/MS was developed a while ago that reflects how develop/update a new decision based on the previous one to model the evolution of the system over time, i.e. dynamic decision-making.
See, e.g. Warren Powell, "Reinforcement Learning and Stochastic Optimization", Wiley, 2022.
What can AI-driven support system add to this methodology? And how do you define what is AI in this context?
Cooperative and competitive/uncooperative game theory was known for a long time. It is based on assumption of rationality of the players. In real life problems, this assumption has been refuted. So, what is the advantage of game theory vs. sequential dynamic decision-making?
Thanks
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Alexander Kolker
ge healthcare
MILWAUKEE WI