Describe your area of study and how it relates to current policy discussions surrounding inequality
My research is in an area I call info-metrics: a framework for rational inference on the basis of imperfect or insufficient information. It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and incomplete information. It provides a logical way for modeling and understanding all types of systems and problems. It yields the simplest possible model, or theory, that fully explains and characterizes the system of interest while accommodating all possible uncertainties. Stated in a complementary way, info-metrics is a framework for processing the available information with minimal reliance on assumptions and information that cannot be validated.
Info-metrics is an interdisciplinary framework positioned at the intersection of information theory, statistical inference, and decision-making under uncertainty within an optimization setting. The rationale behind info-metrics is that the available information for modeling, inference and decision making is insufficient to provide a unique answer (or solution) for decisions and inferences. This implies that there is a continuum of solutions (models, inferences, decisions, ‘truths’) that are consistent with the information (or evidence/data) we have. Therefore, the chosen model, or inference, emerging from the information we have, must be found via a constrained optimization setting. All information enters as constraints and the decision function that chooses the ‘optimal’ solution must satisfy certain properties. Within info-metrics that decision function is an information-theoretic one. In the more commonly used terminology, it is the joint choice of the information used (within the optimization setting) and the decision function employed, that determines the likelihood function.
Inequality measures emerge naturally in models based on the info-metric framework. They are directly linked to the decision function used in the optimization. As a result, once a model is constructed, evaluating the impact of policies and other exogenous effects on the distribution of inequality or any other welfare measure is easily done by studying the Lagrange multipliers that emerge in the optimization. These multipliers, or “shadow prices,” are associated with the information (and data) used in the optimization. They capture the impact of each piece of information on economic inequality. They also provide a way for studying the effect of different policy scenarios, or other exogenous forces, on the economy and its inequality level.
What areas in the study of inequality are most in need of new research?
I think that it would be worthwhile to take a fresh approach to relating inequality measures directly to primitive economic and behavioral concepts. Modeling income (welfare) distribution and economic inequality should be derived from primitive principles of economic and social behavior if we hope to provide insights into the causes and potential solutions to inequality.
The info-metric approach provides a new set of tools for examining these fundamental linkages. It ties inequality measures directly to primitive economic and behavioral concepts through the optimization process. All information enters as constraints within an optimization setting. These constraints connect the basic entities (including behavioral) and the information used. The decision function (defined over the same basic entities) selects the optimal solution. That decision function also captures the level of economic inequality. It arises endogenously and naturally within that framework; it is an information-theoretic quantity. The impact of policy scenarios and other exogenous effects on the economy are, therefore, easily studied.
My hope is that the new tools of the info-metric approach will help in examining these fundamental linkages.
What advice do you have for emerging scholars in your field?
One thing that I believe will yield a successful research career is to not shy away from tackling the big questions and problems. Make sure that the problem is well-defined. Keep an open mind. Learn from others and from other fields and build your model and theory from fundamental principles. Make sure to use the relevant information and make sure it is measurable (even if it’s an indirect measure). It is tough, but it’s worth it.
And most important, follow your heart; work on problems that are interesting to you. That way, even if you aren’t “successful,” at least you won’t be bored!