2020/06/06 by Sasanka Sekhar Chanda, Chanda, Sasanka Sekhar, Sai Yayavaram +1
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Data Visualization and Analytics #Evacuation and Crowd Dynamics #FOS: Biological sciences #FOS: Computer and information sciences #Keywords. algorithm #Multiagent Systems (cs.MA) #NK model #Populations and Evolution (q-bio.PE) #complexity #fitness #interdependence #muddling through #policy making #public administration
paper · pdf · doi:10.48550/arxiv.2006.08333
openalex publication_date 2020/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Under high complexity - given by pervasive interdependence between constituent elements of a decision in an NK landscape - our algorithm obtains fitness superior to that reported in extant research. We distribute the decision elements comprising a decision into clusters. When a change in value of a decision element is considered, a forward move is made if the aggregate fitness of the cluster members residing alongside the decision element is higher. The decision configuration with the highest fitness in the path is selected. Increasing the number of clusters obtains even higher fitness. Further, implementing moves comprising of up to two changes in a cluster also obtains higher fitness. Our algorithm obtains superior outcomes by enabling more extensive search, allowing inspection of more distant configurations. We name this algorithm the muddling through algorithm, in memory of Charles Lindblom who spotted the efficacy of the process long before sophisticated computer simulations came into being.