Inequality as an "emergent property" of Political Economy Systems

Social scientists have debated how to define, measure, and tackle inequality. However, the traditional (aka linear) approach to the problem fails to capture the multidimensionality, polycentricity, and non-linearity that characterize complex phenomena.

To contribute to this growing debate, I make a very brief case favouring a complexity-based approach with the following 7 counter-intuitive ideas:  

[1] Inequality is a fuzzy concept. There is absolute and relative inequality, inequality of outcomes and opportunities, and unfair and fair inequality. A complexity-based approach can help scholars and policymakers propose more accurate operationalizations of the concept.

[2] Inequality is an interdisciplinary domain. It’s related to economics: income, assets, and wealth inequality. It's related to politics: access, voice, and exit. It’s related to sociology: status, group thinking, ownership, and mobility. It’s related to anthropology: values, beliefs, and norms. It’s related to philosophy: freedom, merit, and autonomy. It’s related to psychology: perceptions, attributions, cognition. It’s related to technology: access, divides, and productivity. And the list goes on and on… Complexity can be instrumental in bridging these gaps.

[3] Inequality is a multi-level phenomenon. There is global inequality, inequality among countries, inequality within countries, inequality among groups, intra-group inequalities and inequalities among individuals. There are horizontal and vertical inequalities. And all of them have spillover effects over the others. That makes inequality a wicked problem when assigning ownership–crossing units/levels of analysis (theoretically speaking) and sovereignty/jurisdictions (practically speaking).

[4] Inequality is context and path-dependent. The way inequality expresses itself in a political economy system depends on history: previous interventions, partial successes, and failures of the past. Complexity can help us see why inequality is persistent and whether it is an “actionable” problem.

[5] Inequality is multidimensional. Many root causes, multiple and multi-level interactions, spillover effects, and feedback loops require more than regressions, panel data, systems modelling, or network modelling. Complexity can help us find new metrics and methods. 

[6] Inequality is a complex problem (not simple nor complicated, but complex in nature…). Many root causes mean many plausible entry points for policy interventions. That means there is no critical path, step-by-step guidelines, or program-making policies to treat inequality. Complexity may help us identify policy mixes that are more promising when it comes to a better efficiency-effectiveness balance.

[7] Inequality is polycentric. Many root causes and entry points lead to alternative paths. When modelled as a network, it has many connections, patterns, and architectures. More importantly, there will be several nodes, “net creators” or “net mitigators.” Inequality requires a polycentric governance approach. 

In sum, complexity can help us to see and tackle inequality as a non-linear stochastic emergent property of a political economy system. System modelling can be helpful just for a low-resolution inquiry. Bottom-up interactive-based approaches such as agent-based modelling are promising but need to consider epistemological limitations from assigning specific attributes to individual agents without necessarily considering the transitivity of preferences, bounded rationality, inconsistency of separate judgements, group-thinking, peer-influence, and biases across groups and geographies.