Selected Abstracts

Populism and Its Discontents, 1882-1906

https://www.rivisteweb.it/doi/10.1410/118341

Abstract.  The economic causes of nineteenth-century agrarian unrest in the United States continue to be debated.  This paper examines how Populism was influenced by errors in farmers’ forecasting of crop prices.  These losses are quantified by applying a model that makes minimal assumptions about how farmers formed price expectations.  Both wheat and cotton producing states exhibit a pattern of forecasting errors peaking during the height of the Populist movement in the 1890s. The losses from forecasting error were substantial relative to losses from exogenous factors such as weather and pests. The model is not intended to be a predictor of Populist voting, but rather to highlight an important factor contributing to the farmers’ distress.

Revista di Storia Economica – Italian Review of Economic History (December 2025), pp. 285-306.

Inequality Emerges from Networks

In Qeios, online open access journal with signed reviews, 10/25/2025.

https://www.qeios.com/read/MW5WXQ.2

With William E. Watkins

Economists conventionally attribute inequality in employee compensation to differences in the marginal productivities of workers. However, it is possible that inequality arises from an entirely different source – the network structure of the organizations to which the employees belong. We offer an extremely simple network model that accounts for the degree of inequality observed in modern economies.

Open Access

Minimax-regret climate policy with deep uncertainty in climate modeling and intergenerational discounting

Ecological Economics 201 (November 2022)

(with Charles F. Manski and Alan H. Sanstad)

https://authors.elsevier.com/sd/article/S0921-8009(22)00214-2

Abstract

Integrated assessment models have become the primary tools for comparing climate policies aimed at reducing greenhouse gas emissions. Such policies have often been identified by considering a planner who seeks to make optimal trade-offs between the costs of carbon abatement and the economic damages from climate change. The planning problem has been formalized as one of optimal control, the objective being to minimize the total costs of abatement and damages over a time horizon. Studying climate policy as a control problem presumes that a planner knows enough to make optimization feasible, but in practice, physical and economic uncertainties abound. Manski, Sanstad, and DeCanio (2021) proposed and studied use of the minimax-regret (MMR) decision criterion to account for deep uncertainty in climate modeling. Here we study choice of climate policy that minimizes maximum regret with deep uncertainty regarding both the correct climate model and the appropriate time discount rate to use in intergenerational assessment of policy consequences. The analysis considers a range of discount rates to express both empirical and normative uncertainty about the appropriate magnitude of this parameter. The findings are novel and informative. The MMR analysis points to use of a relatively low discount rate of 0.02 for climate policy. The MMR decision rule keeps the maximum future temperature increase below 2 °C above the 1900–09 level for most of the parameter values used to weight costs and damages.

Open Access

Simple efficiency‑distribution models of production, with an application to robotics

SN Business and Economics (2022) 2:92

https://doi.org/10.1007/s43546-022-00260-z

Abstract
The “efficiency-distribution” model introduced by Houthakker in 1955 offers a flexible approach to production theory that does not require the measurement of capital and other fixed assets. Thus it avoids the theoretical problems associated with the Cambridge controversies and with Franklin Fisher’s critiques of aggregation. The efficiency distribution model can be empirically implemented using only observed productivity distributions and the share of output received by the non-fixed factors. Applications include estimating, for a variety of potential distributions, the  vulnerability of human wages to the introduction of robotic substitutes.

Keywords: Efficiency-distribution model · Production function · Aggregation ·
Artificial intelligence · Robotics

 

Open Access

Addressing partial identification in climate modeling and policy analysis

Proceedings of the National Academy of Sciences, Vol. 118, No. 15 (2021).  https://www.pnas.org/content/118/15/e2022886118.

Co-authors:  Charles F. Manski, Alan H. Sanstad, and Stephen J. DeCanio

Abstract

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including inter-model “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multi-model ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose instead framing climate model uncertainty as a problem of partial identification, or “deep” uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

Significance

Incomplete scientific understanding of the climate and challenges in numerical computation have resulted in numerous climate simulation models being developed and used to generate ensembles, or sets, of climate projections as functions of single greenhouse gas (GHG) emissions scenarios. We propose a “deep uncertainty” approach to framing and analyzing these ensemble model outputs that allows their use in climate-related decision-making without reliance on problematic model weighting schemes. We provide a theoretical framework and an illustrative numerical model to show how single, nearly-optimal decisions can be made regarding GHG abatement without requiring single, average climate model projections.

 

Open Access

 

 

 

 

Can an AI Learn Political Theory?

Abstract

Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” contains much more than its proposal of the “Turing Test.” Turing imagined the development of what we today call AI by a process akin to the education of a child. Thus, while Turing anticipated “machine learning,” his prescience brings to the foreground the yet unsolved problem of how humans might teach or shape AIs to behave in ways that align with moral standards. Part of the teaching process is likely to entail AIs’ absorbing lessons from human writings. Natural language processing tools are one of the ways computer systems extract knowledge from texts. An example is given of how one such technique, Latent Dirichlet Allocation, can draw out the most prominent themes from works of classical political theory.

Keywords: Artificial intelligence, Machine learning, Latent Dirichlet allocation, Alan Turing, Human/AI interactions

AI Perspectives 2(3) (2020)

Click here to view the commentary.

 

Open Access
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