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
Average performance of strategizers who do not reach highest possible performance (global peak) 0.895 [0.894, 0.896] 0.899 [0.898, 0.901] 0.916 [0.915, 0.917] # of activity sets (strategies) evaluated 18.437 [18.232, 18.642] 90.281 [90.012, 90.550] 46.346 [46.299, 46.393] # of changes to get from initial to final strategy 5.713 [5.668, 5.758] 14.764 [14.705, 14.822] 5.322 [5.277, 5.367] Table 3: Strategy changes per focal domain at varying learning thresholds Learning Threshold Level (S=3, D=4) 2 3 4 5 Avg. Total Changes [95% confidence interval] 5.100 [5.060, 5.140] 5.262 [5.218, 5.305] 5.315 [5.270, 5.361] 5.393 [5.347, 5.438] # of changes while focused on first domain 2.925 [2.903, 2.948] 3.621 [3.593, 3.649] 3.912 [3.879, 3.946] 4.057 [4.022, 4.093] # of changes while focused on second domain 1.501 [1.476, 1.526] 1.145 [1.120, 1.170] 0.969 [0.945, 0.992] 0.909 [0.887, 0.932] # of changes while focused on third domain 0.674 [0.655, 0.693] 0.496 [0.479, 0.513] 0.434 [0.418, 0.451] 0.426 [0.410, 0.442]
Table 4: Summary of strategy problem experiment results Experimental input Complexity Problem size Number of domains when complexity is high Between domains (Kb) Within domains (Kw) Number of decisions per domain (D) Number of domains (S) Best process when Low value Decision weaving Decision weaving & Chunky search Decision weaving, Chunky search, & Local search Decision weaving Decision weaving Best process when High value Decision weaving Decision weaving Decision weaving Decision weaving & Chunky search Decision weaving As value increases the performance gap… Decreases initially and then remains steady Increases Increases Decreases to nothing Decreases but holds