Opening the stacks
Opening the stacks
A modeling approach that represents interactive decisions involving multiple actors and linked arenas, capturing reflexive and interdependent effects.
3 chapters across 1 book
The chapter explores the use of hypergame models to represent complex interactive decision-making processes involving multiple actors and arenas, emphasizing the importance of capturing differing perceptions and reflexive links. It advocates a mixed-scanning, iterative, and piecemeal modeling approach that balances complexity with tractability, relying heavily on clients' tacit knowledge and collaborative problem structuring methods such as cognitive mapping. The integration of hypergames with other decision-making frameworks and the iterative refinement of models through client-analyst collaboration are highlighted as key strategies for managing uncertainty, preferences, and strategic complexity.
The chapter presents a detailed application of problem structuring methods to analyze a complex decision-making scenario involving English and Welsh sailing associations and their championship arrangements. It employs argumentation maps and tableau models to identify feasible outcomes and rank preferences, revealing unexpected possibilities and leading to model simplifications. The analysis further incorporates hypergame modeling to capture differing perceptions, timing uncertainties, and strategic interactions, focusing on the critical dilemma faced by the English committee of whether to wait for Welsh decisions or proceed independently, thereby illustrating the importance of considering multiple viewpoints and managing complexity in conflict resolution.
The chapter presents a detailed analysis of a hypergame model addressing the strategic decisions of the English and Welsh Associations regarding championship announcements, highlighting the risks of misperceptions and potential constitutional crises. It illustrates an iterative, interactive decision-making process that balances crisis management with maintaining inter-association relations, emphasizing the value of adaptive problem structuring methods such as mixed scanning and iterative learning to manage uncertainty and conflict effectively.