PROBLEM FRAMING BEFORE SATISFICING DECISION MAKING IN RURAL CYBER-PHYSICAL SOCIAL SYSTEMS

Abstract

In this thesis, the author develops a Context-Aware Control System (CACCS) framework for the adaptive and satisficing design of rural cyber-physical-social systems (rural CPSS). Rural service systems, including groundwater allocation networks, off-grid microgrids, and primary healthcare clinics, operate under uncertainty, limited resources, and locally embedded practices that conventional optimization approaches cannot reliably represent. The author addresses the structural translation gap in this thesis: the absence of a replicable method for translating the structural components of systems archetypes into operational decision formulations. In the CACCS framework, the author integrates the Dilemma Triangle Method, causal loop diagram construction, systems archetype interpretation, an archetype quantification method, and the compromise Decision Support Problem within a three-layer architecture comprising a Core Layer, a Semi-Core Layer, and an Application Layer. Decision-makers use archetype quantification to translate balancing loops into service-target goal equations, reinforcing loops into degradation penalties and feasibility constraints, structural tensions into weighted trade-offs, and delayed feedback effects into repeated closed-loop operational constraints. The author demonstrates the framework through three rural test problems, each governed by a distinct systems archetype: a photovoltaic battery microgrid under the Fixes That Fail archetype, a groundwater allocation system under the Tragedy of the Commons archetype, and a primary healthcare clinic under the Shifting the Burden archetype. For each test problem, the author constructs a causal loop diagram, interprets it through its governing archetype, translates the structure into a compromise Decision Support Problem, and evaluates satisficing policies against archetype-unaware baseline policies through closed-loop execution across a thirty-day decision horizon. Across the three test problems, designers observe that decision makers use CACCS satisficing policies to preserve structural state variables, shift shortfalls in directions consistent with the governing weight structure and avoid the structural failure modes that researchers associate with archetype-unaware baseline policies. In this thesis, the author develops a problem-framing method for rural cyber-physical-social systems. The author shows how decision-makers recognize the structural tension in a rural service problem before they formulate a satisficing operational decision. Decision-makers use archetype quantification to translate that frame into decision variables, constraints, deviation terms, weights, and state updates. Decision-makers then use the compromise Decision Support Problem to formulate satisficing actions that account for uncertainty, limited resources, local knowledge, and stakeholder priorities.