Mini Review Volume 1 Issue 5 - September 2018 DOI: 10.19080/ASM.2018.01.555572 Ann Soc Sci Manage Stud Copyright © All rights are reserved by Benyamin Lichtenstein Bringing Complexity into Social Analysis: Three Principles from Emergence Benyamin Lichtenstein Benyamin Lichtenstein* Department of Management and Marketing, University of Massachusetts, Boston, USA Submission: May 30, 2018; Published: September 26, 2018 * Corresponding author: Benyamin Lichtenstein, Department of Management and Marketing, University of Massachusetts, Boston, USA, Email: Introduction A growing set of models and theories are integrating the dynamics of complex systems into their analyses. These approaches diverge from the classic, deterministic methods that have dominated the social sciences since the 1960s. An exemplar of these new models is Effectuation [1]. which emphasizes the resources an entrepreneur has, and builds on those step by step, until a unique outcome comes into focus. Such outcomes are not predictable but may be far beyond what was originally expected. Another exemplar is computational complexity science; all the models affiliated with the Santa Fe Institute [2,3]. These include NK Landscapes [4]. Complex Adaptive Systems [5]. Genetic Algorithms and other systems [6]. which have helped reveal the interdependence of agents in simulated environments. A more recent example is Imagineering, a book in press by which examines a special emergent state called Collective Creativity, in which an entire group together generates a new artistic expression or concept [7]. notion of Bricolage also draws on this idea, where the emergence of the system or entity is a kind of open-ended assemblage, with outcomes that may not be foreseen. These are paralleled in my work on Generative Emergence [8,9]. which explores the drivers of emergence and compares them with others. My study of emergence started with systems scientists like [10-13]. over 35 years I have sought the core characteristics of emergence in social systems. Here I present the three that are most central. Non-Linearity In our training as social scientists, one core assumption is that the world has linear causality: A unit of action should lead to a corresponding unit of outcome. However, it turns out that our world is filled with non-linear systems, in which certain interactions have much higher impact in the system than do others [14]. This can be visualized using the “80/20 rule”: Eighty percent of the system is ‘ruled’ by just 20% of the agents, i.e. a small percentage of the system has the greatest effect. Thus, the influence of the top 20%—really of the top 1%—is amplified to a remarkable degree. The assumption of non-linearity leads to other changes in our theorizing. For example, non-linear systems have leverage points: Specific places where an intervention has a dramatically increased outcome. Here, the system’s non-linearity is strongly expressed. Although rare, leverage points have tremendous impact. Another expression of non-linearity is in trigger points or thresholds of emergence; these have been shown by [15-19]. amongst others. Once you hit a threshold of change, the system enacts a rapid creation of new order. Agent Interdependence Another key shift in these new dynamic models, is the interdependence across agents; the ways in which each one’s influence is felt by the other, such that our interactions are deeply intertwined. In contrast, traditional social science emphasized the independence of agents, such that a researcher could examine a system without really influencing it. However, in dynamic and natural systems, an agent’s identity—as part of a group or community or network—is created by the presence of all the other agents in the system. Agents are co-creative, co- emergent, co-dependent on each other for their existence. Ann Soc Sci Manage Stud 1(5): ASM.MS.ID.555572 (2018) 0098 Abstract Systems are increasingly complex, and traditional theories and constructs don’t consider the dynamics of the social world. Through 60+ published papers exploring these complexity dynamics, I have summarized the differences into three core insights: Non-linearity, Interdependence, and Emergence. This brief review summarizes how to use each of these insights, and how they can reveal dynamics that are important but mostly hidden. Keywords: Social; Deterministic; Dynamics; Genetic; Bricolage; Tremendous; Traditional; Co-Creative; Emphasizes; Discipline; Trigger; Leverage