Table 1: Three strategy formation processes combining modular and integrated problem solving Local search Chunky search Decision weaving Process Change one Focus on subset of Focus on single domain summary component, evaluate decisions, change one (Sequential focus) until system performance, component in subset, several performance accept if an evaluate subset improving changes made. At improving change, performance, accept if an same time, periodically repeat till optimum improving change, repeat explore local change in other till optimum then expand domain (Stepping stones). subset Repeat till all domains addressed Exploration Moderately modular Modular first (1 change at Simultaneously Modular for new (1 change at a time a time across a subset of (Sequential focus) & strategic across all possible possibilities) -> Integrated (Opportunistic activities changes) Moderately modular later stepping stones) (Local search) Always Integrated Modular first -> Integrated Modular (Learning plateaus to Evaluation later (Gradually increasing switch) & Integrated (overall of strategy integration) performance) simultaneously At a (local) optimum Fully integrated, at a Basic structure of domain Stop (local) optimum understood, all domains addressed Pros Strategy remains Avoid getting stuck in More depth to exploration of coherent. local peak early, modules, but avoids Fast. eventually is fully integrated Fast & high-performing Get on local Slow, too integrative in Chance of misfit in highly Cons “stuck” peaks early low complexity complex environments Table 2: Core results (N=12, S=3, D = N/S = 4, Kw=0.75, Kb=0.25, L=4, and ss_prob=0.75) - 95% confidence intervals are shown in brackets Searcher Local searcher Chunky searcher Decision weaver Average Performance 0.904 0.908 0.927 [0.903, 0.905] [0.907, 0.909] [0.926, 0.928] % of searchers to reach 8.52 0.871 13.32 highest possible performance (global peak)
Table 1: Three strategy formation processes combining modular and integrated problem solving Local search Chunky search Decision weaving Process summary Change one component, evaluate system performance, accept if an improving change, repeat till optimum Focus on subset of decisions, change one component in subset, evaluate subset performance, accept if an improving change, repeat till optimum then expand subset Focus on single domain (Sequential focus) until several performance improving changes made. At same time, periodically explore local change in other domain (Stepping stones). Repeat till all domains addressed Exploration for new strategic activities Moderately modular (1 change at a time across all possible changes) Modular first (1 change at a time across a subset of possibilities) -> Moderately modular later (Local search) Simultaneously Modular (Sequential focus) & Integrated (Opportunistic stepping stones) Evaluation of strategy Always Integrated Modular first -> Integrated later (Gradually increasing integration) Modular (Learning plateaus to switch) & Integrated (overall performance) simultaneously Stop At a (local) optimum Fully integrated, at a (local) optimum Basic structure of domain understood, all domains addressed Pros Strategy remains coherent. Fast. Avoid getting stuck in local peak early, eventually is fully integrated More depth to exploration of modules, but avoids Fast & high-performing Cons Get “stuck” on local peaks early Slow, too integrative in low complexity Chance of misfit in highly complex environments Table 2: Core results (N=12, S=3, D = N/S = 4, Kw=0.75, Kb=0.25, L=4, and ss_prob=0.75) - 95% confidence intervals are shown in brackets Searcher Local searcher Chunky searcher Decision weaver Average Performance 0.904 [0.903, 0.905] 0.908 [0.907, 0.909] 0.927 [0.926, 0.928] % of searchers to reach highest possible performance (global peak) 8.52 0.871 13.32
Strategy-Formation-as-Solving-a-Complex-and-Novel-Problem.docx