Sociological theories of crime include: theories of strain blame crime on personal stressors; theories of social learning blame crime on its social rewards, and see crime more as an institution in conflict with other institutions rather than as in- vidual deviance; and theories of control look at crime as natural and rewarding, and explore the formation of institutions that control crime. Theorists of corruption generally agree that corruption is an expression of the Patron-Client relationship in which a person with access to resources trades resources with kin and members of the community in exchange for loyalty. Some approaches to modeling crime and corruption do not involve an explicit simulation: rule based systems; Bayesian networks; game theoretic approaches, often based on rational choice theory; and Neoclassical Econometrics, a rational choice-based approach. Simulation-based approaches take into account greater complexities of interacting parts of social phenomena. These include fuzzy cognitive maps and fuzzy rule sets that may incorporate feedback; and agent-based simulation, which can go a step farther by computing new social structures not previously identified in theory. The latter include cognitive agent models, in which agents learn how to perceive their en- ronment and act upon the perceptions of their individual experiences; and reactive agent simulation, which, while less capable than cognitive-agent simulation, is adequate for testing a policy's effects with existing societal structures. For example, NNL is a cognitive agent model based on the REPAST Simphony toolkit.

Sociological theories of crime include: theories of strain blame crime on personal stressors; theories of social learning blame crime on its social rewards, and see crime more as an institution in conflict with other institutions rather than as in- vidual deviance; and theories of control look at crime as natural and rewarding, and explore the formation of institutions that control crime. Theorists of corruption generally agree that corruption is an expression of the Patron-Client relationship in which a person with access to resources trades resources with kin and members of the community in exchange for loyalty. Some approaches to modeling crime and corruption do not involve an explicit simulation: rule based systems; Bayesian networks; game theoretic approaches, often based on rational choice theory; and Neoclassical Econometrics, a rational choice-based approach. Simulation-based approaches take into account greater complexities of interacting parts of social phenomena. These include fuzzy cognitive maps and fuzzy rule sets that may incorporate feedback; and agent-based simulation, which can go a step farther by computing new social structures not previously identified in theory. The latter include cognitive agent models, in which agents learn how to perceive their en- ronment and act upon the perceptions of their individual experiences; and reactive agent simulation, which, while less capable than cognitive-agent simulation, is adequate for testing a policy's effects with existing societal structures. For example, NNL is a cognitive agent model based on the REPAST Simphony toolkit.

This book examines the theoretical and conceptual foundation of effective modern intelligence collection-the strategies required to support intelligence analysis of the modern, complex operational environments of today's military conflicts or competitive civilian situations such as business. Just as the old rules of conventional warfare and intelligence analysis do not apply fully in the 21st-century environment, neither does the traditional methodology of collecting intelligence on these elusive, adapting foes operating as complex adaptive systems (CAS)-adversaries that excel in today's complex contexts. Intelligence Collection: How To Plan and Execute Intelligence Collection In Complex Environments proposes substantive improvements in the way the U.S. national security system collects intelligence and supports intelligence analysis. The work draws on the groundbreaking work of a diverge group of theorists ranging from Carl von Clausewitz and Sun Tzu to M. Mitchell Waldrop, General David Petraeus, and Orson Scott Card, communicating a unifying theory and ontology of thought for how America's intelligence collection professionals must learn to collect data as our country faces elusive, determined, and smart adversaries in nonlinear, dynamic environments. The new ideas presented will help the nation's intelligence collection specialists to amass a formidable, cumulative intelligence power, regardless of the level of war or the type of operational environment.

This book offers a vast conceptual and theoretical exploration of the ways intelligence analysis must change in order to succeed against today's most dangerous combatants and most complex irregular theatres of conflict. Intelligence Analysis: How to Think in Complex Environments fills a void in the existing literature on contemporary warfare by examining the theoretical and conceptual foundations of effective modern intelligence analysis—the type of analysis needed to support military operations in modern, complex operational environments. This volume is an expert guide for rethinking intelligence analysis and understanding the true nature of the operational environment, adversaries, and most importantly, the populace. Intelligence Analysis proposes substantive improvements in the way the U.S. national security system interprets intelligence, drawing on the groundbreaking work of theorists ranging from Carl von Clauswitz and Sun Tzu to M. Mitchell Waldrop, General David Petraeus, Richards Heuer, Jr., Orson Scott Card, and others. The new ideas presented here will help the nation to amass a formidable, cumulative intelligence power, with distinct advantages over any and all adversaries of the future regardless of the level of war or type of operational environment.