The Complexity of System Effects

Bibliographic Data

ID6251489
AuthorsAndrea Jones-Rooy (Carnegie Mellon University), Sarah E Page (0000-0003-2723-5216), Scott E Page
Year2012
Volume24
Issue3
Pages313-342
Publication date2012-09-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueCritical Review (JOURNAL)
Journal identifiersISSN: 0891-3811 • E-ISSN: 1933-8007
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/08913811.2012.767045
OpenAlexW2031017753
LanguageEN
Citations received8
References cited37

Complexity science has witnessed a number of advances since the publication of Jervis's System Effects. These advances better allow us to untangle the messy elements in a system and predict sets of likely outcomes. However, just because a system is complex doesn't mean that all the ideas relating to complexity—such as agent-based modeling, path dependency, tipping points, between-class versus within-class effects, and networks—are necessarily relevant. One of our tasks is to determine whether they are—and, if so, their implications. As examples, we use China's role in the formation of the United States housing bubble; the federal government's bailout of AIG and Bear Stearns but not Lehman Brothers; and China's failure to experience a regime change such as the Middle East's Arab Spring. Acknowledgments thank Philip B. K. Potter, Kyle Joyce, Ken Kollman, and Jeffrey Friedman for comments on earlier versions. Research for this essay was funded by the U.S. Army Research Office. Notes 1. For another example of systems at work across domains and at a global level, see the description in Zolli and Healy Citation2012 (1–4) of the causes and consequences of Mexico's food riots in 2007. 2. For an excellent review of the contributions of systems thinking toward climate change see Edwards Citation2010. 3. See Page Citation2010 and Mitchell Citation2009 for surveys of these advances in measuring complexity. 4. Jervis made no claim of originality for either of these observations. The often-curious links between micro-level behaviors and macro-level outcomes, popularized by Schelling, can be traced to Arrow, Nash, and Condorcet. The idea that we cannot consider one domain in isolation goes back to von Forrestor. 5. A related precursor with a focus on informational cascades is Bikhchandani et al. Citation1992. See Kollman and Page Citation2005 for a survey of agent-based models in political science. 6. We thank Philip B. K. Potter for this point. 7. For even more details on the role of structure in networks see Jones-Rooy and Page Citation2010. 8. We don't know whether the government used a related analysis in making its decision. We note only that a consideration of systems effects suggests a set of actions similar to those chosen. 9. It's not as simple as just saying "build this factory here"—although that can happen, too. Often economic or tax incentives in Special Economic Zones in China can be the way the government influences where particular industries end up. 10. It is beyond the scope of this essay to analyze the current system, but many analytical tools, including agent-based modeling and dynamic mathematical techniques, can guide scholars in understanding it. We could conduct a similar analysis in search of evidence of a likely bifurcation in the system, or of a phase transition. Ideally, physicists and international-relations scholars might combine their expertise on this matter. 11. The tip was also a between-class tip as it was a move from a stable equilibrium to a complex one. 12. Page Citation2008 distinguishes between uncertainty, difficulty, and complexity. Jervis often means a mixture of complexity and uncertainty

Bailout · China · Economics · Financial crisis · Government (linguistics · Law and economics · Operations research · Originality · Political science · Sociology · Complex Network Analysis Techniques · Complex Systems and Time Series Analysis · History · Law · Mathematics · Opinion Dynamics and Social Influence · Philosophy

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