About

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School of the University of Pennsylvania. His research interests include artificial intelligence and computational rationality, decision support systems, evolutionary computation such as genetic algorithms and metaheuristics for constrained optimization, logic modeling, agent communication languages, and text mining. His work explores the application of computational methods to complex decision-making and strategic behavior in social sciences and markets. Kimbrough's scholarly contributions include empirical validation of models in social sciences, such as electoral redistricting, and the development of agent-based models for strategic market analysis, including electricity markets. He has presented on topics like solution pluralism, optimization, and the use of subjective judgments in modeling. His research emphasizes the integration of computational techniques with social science applications, aiming to extract more value from optimization models and improve decision-making processes in various domains.

Research topics

• Business
• Political Science
• Sociology
• Natural resource economics
• Economics
• Computer Science
• Environmental resource management
• Law
• Chemistry
• Accounting
• Environmental science
• Finance
• Environmental economics
• Electrical engineering
• Engineering

Selected publications

• Aggregator electricity price guarantees for households with flexibility potential utilizing thermal building inertia

  Applied Energy · 2026-02-04

  articleOpen access

  This study introduces an approach to mitigate the reluctance of households to adopt dynamic electricity tariffs by proposing individual price contracts tailored to household characteristics. These contracts guarantee individual electricity rates to households with flexibility potential, such as thermal or electrical storage and the thermal mass of buildings, in exchange for granting aggregators operational control. The household-specific contracts are determined and evaluated through a three-step process, combining deterministic and stochastic modeling. First, an optimization problem for the operation of home energy management systems is formulated. The proposed model incorporates the thermal inertia of buildings as a flexibility potential, an aspect frequently overlooked in existing studies. Then, a Monte Carlo simulation of household parameter combinations is run, followed by a quantile regression prediction of household-level low-price guarantees. The simulations of 9404 household configurations in Germany demonstrate that aggregator-managed flexibility consistently lowered electricity costs by an average of 7.36% (2.5 ct/kWh) compared to static tariffs, with 78.4% of households achieving rates below the competitive retail benchmark. Aggregators also realized higher profitability on a per-household basis across all three analyzed years compared to scenarios without flexibility control. Our results demonstrate that building parameters, particularly thermal inertia, substantially influence the available flexibility potential and should be considered a key factor in the design of household-level guarantee contracts. The study contributes to understanding and quantifying uncertainty in dynamic tariffs for households, aiming to advance the utilization of household demand response potential in modern power markets. • Developed a three-stage method to calculate price guarantees under uncertainty. • Aggregators guarantee individual fixed tariffs for constrained control of flexibility. • Monte Carlo simulation of 9404 households with varying endowments and preferences. • Aggregator-control reduced electricity costs by an average of 7.36% (2.5 ct/kWh). • Building characteristics strongly shape flexibility potential and guarantee values.

  PublisherDOI

• Aggregator electricity price guarantees for households with flexibility potential utilizing thermal building inertia

  Repository KITopen (Karlsruhe Institute of Technology) · 2026-01-01

  articleOpen access
  PublisherDOI

• Aggregator Price Guarantees for Households with Flexibility Potential and Thermal Building Inertia

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• On Large Language Models as Data Sources for Policy Deliberation on Climate Change and Sustainability

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

• Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling

  ArXiv.org · 2025-02-12

  preprintOpen accessSenior author

  Energy systems modeling frequently relies on time series data, whether observed or forecast. This is particularly the case, for example, in capacity planning models that use hourly production and load data forecast to occur over the coming several decades. This paper addresses the attendant problem of performing sensitivity, robustness, and other post-solution analyses using time series data. We explore two efficient and relatively simple, non-parametric, bootstrapping methods for generating arbitrary numbers of time series from a single observed or forecast series. The paper presents and assesses each method. We find that the generated series are both visually and by statistical summary measures close to the original observational data. In consequence these series are credibly taken as stochastic instances from a common distribution, that of the original series of observations. With climate change in mind, the paper further proposes and explores two general techniques for systematically altering (increasing or decreasing) time series. Both for the perturbed and unperturbed synthetic series data, we find that the generated series induce variability in properties of the series that are important for energy modeling, in particular periods of under- and over-production, and periods of increased ramping rates. In consequence, series produced in this way are apt for use in robustness, sensitivity, and in general post-solution analysis of energy planning models. These validity factors auger well for applications beyond energy modeling.

  PublisherOA PDFDOI

• On Representing Knightian Uncertainty in Agent-Based Models

  Springer proceedings in complexity · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Ramifications of Widespread Adoption of Battery Electric Vehicles (BEVs)

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Aggregator Price Guarantees for Households with Flexibility Potential and Thermal Building Inertia

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Climate Change and Sustainability Policies: Report from an Exploratory Study on Values Elicitation, version 1.1

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Agent-Based Foraging or When Imperfect Unbiased knowledge Yields Systematic Deviation from Ideally Rational Outcomes

  Springer proceedings in complexity · 2024-01-01 · 1 citations

  book-chapter
  PublisherDOI

Frequent coauthors

• Frederic H. Murphy

  Temple University

  21 shared

• Hoong Chuin Lau

  19 shared

• Scott A. Moore

  Galois (United States)

  14 shared

• Robin Clark

  14 shared

• Christine Chou

  National Dong Hwa University

  13 shared

• Garett Dworman

  Building Bridges

  12 shared

• Hemant K. Bhargava

  University of California, Davis

  12 shared

• David Harlan Wood

  University of Delaware

  10 shared