DOI
10.2139/ssrn.1889359

Summary
The article presents a small summary of the implications deriving from the increasing complexity evidenced in social, political and economic systems. Page concentrates his analysis on the opportunities opened by the study of complexity in social sciences. He believes that, once the social sciences will have overcome the traditional analysis based on linear regressions and static equilibrium, they may be able to “limit the damage of large events and to harness complexity to produce better social outcomes”. To reach this objective, the author advocates for the implementation of 4 important changes in social sciences’ practices:
- Advancing the methodologies for measuring and categorizing the complexity of social processes;
- Promoting interdisciplinary research on specific problems (e.g. improving education)
- Rethinking variation and diversity
- Advance computational agent-based modelling

Relevance for Complex Systems Knowledge
This article presents a rather interesting and original perspective on how to integrate complexity in the social sciences. The 4 changes advocated by the authors imply some rather original insights:
- By advancing methodologies and the capacity to measure complexity in social processes, Page invites to establish new kind of measurements specific for the social sciences. This entails a rather new perspective in the analysis of social processes, as it would invite policy makers to manage also the level of complexity in a process.
- The promotion of interdisciplinary research invites the adoption of a generative perspective that see social outcomes as produced by purposive actors responding to incentives, information, cultural norms, and psychological predispositions.
- The third “change” derives from the second, as the generative perspective also imply a different perspective on diversity. Page invites researchers to see variations as central aspect of social processes, instead of doing like statisticians, who perceive variation from average effects.
- Finally, the advancement of computation agent-based modelling invites to better understand their outcomes. Generally speaking, ABM produce 4 kind of outcomes: static equilibria, periodic equilibria (patterns), random paths or complex trajectories. Enhancing their modelling should improve our understanding of why one process generates one outcome instead of another.

Point of Strength
- Rather original and convincing
- It provides suggestions on how to improve social sciences through the adoption of a more interdisciplinary and advanced methodology
- It presents a clear goal for the adoption of complexity in social science (i.e. policy design and management of complex processes)