An engineering perspective for policy design: self-organizing crime as an evolutionary social system

Abstract

Here we introduce methodological guidelines for designing policies against organized crime. We employ the evolutionary ontology proposed by Kurt Dopfer for conceiving organized crime as the outcome of social, intelligent agents whose strategies evolve through time. To illustrate the use of this ontology we explore the case of corruption in public procurement processes in Colombia in which criminal organizations—groups of corrupt agents—converge spontaneously. The ontology leads to conceive corruption as a knowledge process that adapts according to the evolution of problem-solving rules that are created, used and discarded by agents that seek to attain personal gains by means of public resources. We also use an engineering perspective that favors model-aided design. We built a simulation model that illustrates how the dynamics of such evolving-rules systems can be conceptualized for exploring potential policies. The application of the evolutionary ontology shows why corruption exhibits self-organization: system-level patterns develop from spontaneous interactions that use only local information. Rule dynamics form a changing structure of rule-populations that adapt to novel environmental conditions and generate meta-stable adaptions that explain why corruption persists despite continuous challenges from the environment. This engineering approach forms the ground for proposing policies that instead of addressing the operant level of a social system (according to observed operations and data), should meet the dynamics of rules that govern those operations. Hence, the role of regulators shifts from “controllers” to inventors of selectionist environments that facilitate suitable change through the introduction or promotion of counter-crime rules, the design of selective pressures that favor the evolution of desirable rules and the attention to coordination gaps at the macro-structure. The recognition of organized crime as the outcome of an evolving-rules system changes the questions that orient policy-making and focuses on the redesign of evolving knowledge. Accordingly, our methodological guidelines address such evolutionary dynamics and can be applied to several forms of organized crime.