Short Bio: Leigh Tesfatsion
Formal Short Bio
Leigh Tesfatsion received the Ph.D. degree in economics from the University of Minnesota, Mpls., in 1975, with a minor in mathematics. She is Research Professor of Economics, Professor Emerita of Economics, and Courtesy Research Professor of
Electrical & Computer Engineering, all at Iowa State University. Her principal current research areas are electric power market design and the development of Agent-based Computational Economics (ACE) platforms for the performance testing of these designs.
She is the recipient of the 2020 David A. Kendrick Distinguished Service Award from the Society for Computational Economics (SCE) and an IEEE Senior Member. She has served as guest editor and associate editor for a number of journals, including the IEEE Transactions on Power Systems, the IEEE Transactions on Evolutionary Computation, the Journal of Energy Markets, the Journal of Economic Dynamics and Control, the Journal of Public Economic Theory, and Computational Economics.
Research History: A Winding Interdisciplinary Path
Thesis research at the University of Minnesota (1974-5) on Games, Goals, and Bounded Rationality.
Leigh Tesfatsion, "Two Essays on Individual Choice"
PhD Thesis, Department of Economics, 76-15,007 University of Minnesota, Mpls., December 1975.
The key idea of this thesis is that boundedly-rational decision-makers can supplement partial contingency plans with goals,
thus permitting actions to be undertaken in desired directions despite the absence of complete information. An expected utility model permitting goal-directed action choice is formulated and axiomatized for concrete demonstration.
Work at the University of Minnesota (1974-5) and University of Southern California (1975-1984) on
The following question arises for sequential decision-making under uncertainty: Can the associative mapping between decisions and expected payoffs be directly updated, without recourse to the updating of probabilities?
Criterion Filtering (CF) provides an affirmative answer to this question. CF is a learning algorithm that permits the direct updating of expected utility on the basis of sequentially realized utility outcomes, conditional on prior (initial) utility assessments. The CF algorithm thus provides a "Bayes' Rule for Utility".
Criterion Filtering (CF): A Dual Approach
to Bayesian Inference and Adaptive Control
Work at University of Southern California (1975-1990) with applied mathematician Bob Kalaba on Flexible Least Squares.
- Flexible Least Squares (FLS) is a multicriteria optimization method for model specification. The goal of the FLS method is to identify the "Pareto frontier" of all efficiently estimated models conditional on: (i) a given theory; (ii) a given data set; and (iii) a designated collection of goodness-of-fit metrics. FLS does not require the imposition of problematic stochastic assumptions on "residual error terms" that in fact arise from deterministic model misspecification.
Flexible Least Squares (FLS): A Multicriteria Optimization Method for Model Specification
Work at USC (1975-1990) with applied mathematician Bob Kalaba on Adaptive Computation.
An adaptive computation method adapts to the problem at hand rather than requiring the problem to be adapted to the method.
Bob Kalaba and I developed adaptive computation methods for the solution of various types of nonlinear problems.
For example, we developed an adaptive homotopy solution algorithm able, in runtime, to detect and avoid regions where calculations are ill-conditioned due to nearby singularities or bifurcation points. This adaptive capability is achieved by replacing the standard homotopy continuation parameter, moving in a pre-set manner from 0 to 1 along the real line, with a "smart agent" able to construct and traverse an adaptively determined path from 0+0i to 1+0i in the complex plane. The smart agent decides the direction and length of its next step, given its current state, by solving a multi-criteria optimization problem requiring a trade-off between two criteria: (i) maintain a short path length from 0+0i to 1+0i; and (ii) avoid regions in the complex plane where ill-conditioning of calculations is detected.
Adaptive Computation Methods for Nonlinear Systems;
Work at USC (1975-1990) and ISU (1990-present) on
Modeling of Economies as Open-Ended Dynamic Systems.
- This work includes, for example, the modeling of macroeconomies as DSGL systems, where
DSGL = DSG(~E) + Learning Agents.
More precisely, DSGL systems are Dynamic,
Stochastic, and General market-based macroeconomic systems for which decision-making agents have Learning capabilities. Coordination arises endogenously (if at all) in DSGL systems as a result of successive agent interactions, not from the external imposition of equilibrium assumptions.
Optimality and Efficiency of
Open-Ended Dynamic Economies
Work at ISU (1990-2001) on the Blending of Game Theory with Matching Theory.
Trading games are explored for concrete demonstration. The endogenous preferential choice and refusal of trading partners (matching theory) is modeled, together with the evolution of trading strategies (game theory). At each point in time the selection of trading partners, and the selection of trading strategies for use with these partners, are determined on the basis of past trade outcomes and initially held beliefs.
The Trade Network Game (TNG) Laboratory is open-source demonstration software permitting the beautiful run-time visualization of endogenous trade-network formation among strategic buyers, sellers, and dealers in a sequential trade network game.
Endogenous Trade Network Formation
and the TNG Laboratory;
Work at ISU (1990-present) on
completely Agent-Based Modeling (c-ABM) and
Agent-based Computational Economics (ACE).
Scientists and engineers seek to understand how real-world systems work, and how real-world systems could work better. Any modeling approach devised for such purposes must simplify reality. Ideally, however, the modeling approach should be flexible as well as logically rigorous; it should permit model simplifications to be appropriately tailored for the specific purpose at hand.
Modeling flexibility and logical rigor have been the two key goals motivating my development of completely Agent-Based Modeling (c-ABM)
and Agent-based Computational Economics (ACE). The
c-ABM modeling approach is a variant of agent-based modeling characterized by seven modeling principles. Any model adhering to these seven modeling principles is a computational laboratory permitting explorations of computational systems in a manner analogous to biological experimentation with cultures in Petri dishes. The ACE modeling approach is a specialization of c-ABM to economic systems.
Completely Agent-Based Modeling (c-ABM)
Economics (ACE): Homepage
Work at ISU (2011-present) on a Linked Swing-Contract Market Design for Wholesale Electric Power Markets
Good market design begins with an appropriate conceptualization of the product being transacted. For centrally-managed wholesale electric power markets operating over high-voltage transmission grids, this product is flows of power ("power-paths") injected and/or withdrawn at specific grid locations during specific operating periods.
In the Wiley/IEEE Press book cited below, a linked swing-contract market design is proposed for centrally-managed wholesale power markets to facilitate increased reliance on renewable power and demand-side participation. The proposed swing contracts are firm or option two-part pricing contracts permitting resources to offer the just-in-time availability of diverse dispatchable power-paths at designated grid locations during designated future operating periods.
L. Tesfatsion (2021), A New Swing-Contract Design for Wholesale Power Markets, 20 Chapters, 288pp., John Wiley & Sons, Inc. (IEEE Press Series on Power Engineering), Hoboken, NJ, USA.
(Intro, Table of Contents, & Refs),
(Slide-Set Overview: ARPA-E/I-CPIE Talk),
(Wiley Book Flyer).
Work with electric power engineers at ISU (2000-present) on market-based designs for Integrated Transmission and Distribution Systems.
The growing reliance of centrally-managed wholesale electric power markets on renewable power resources poses new challenges for these markets. For example, intermittent resources such as wind power and solar power that are not back-stopped by storage can make it more difficult to balance power withdrawal (usage & losses) with power injection (supply) across a transmission grid, a prerequisite for reliable grid operations.
These challenges have led to efforts by the U.S. Federal Energy Regulatory Commission -- such as FERC Order 2222 (Final Rule) released in September 2020 -- to increase the participation of distributed power resources in these markets in dispatchable aggregated form. Transactive Energy System (TES) design is a relatively new approach to electric power management that could provide important support for FERC Order 2222 objectives.
In response to these trends, our ISU project on
Integrated Transmission and Distribution (ITD) systems has developed
various types of TES designs to facilitate the efficient reliable provision of power and ancillary services from a wide array of ITD power resources in return for appropriate compensation. This ITD TES design research has been supported by the development of agent-based computational platforms representing the salient operations of actual electric power systems.
Integrated Transmission and Distribution (ITD)
Project: ISU Homepage
Leigh Tesfatsion (2021), "Agent-Based Computational Economics: Overview and Brief History"
invited chapter for: R. Venkatachalam
(Ed.), 2022. Artificial Intelligence, Learning and Computation in Economics and Finance,
Springer, to appear.
Leigh Tesfatsion (2021), A New Swing-Contract Design for Wholesale Power Markets, 20 Chapters, 288pp., John Wiley & Sons, Inc. (IEEE Press Series on Power Engineering), Hoboken, NJ, USA.
(Table of Contents/Intro/Refs),
(Slide-Set Overview: ARPA-E/I-CPIE Talk),
(Wiley Book Flyer)
Swathi Battula, Leigh Tesfatsion, and Zhaoyu Wang (2020), "A Customer-Centric Approach to Bid-Based Transactive Energy System Design"
IEEE Transactions on Smart Grid 11(6), 4996-5008.
Leigh Tesfatsion (2018), "Electric Power Markets in Transition: Agent-Based Modeling Tools for Transactive Energy Support"
Chapter 13 (pp. 715-766) in Cars Hommes and Blake LeBaron (eds.), Handbook of Computational Economics 4: Heterogeneous Agent Models, Handbooks in Economics Series, North Holland (Elsevier), Amsterdam, the Netherlands.
- Leigh Tesfatsion (2017), "Modeling Economic Systems as
Locally-Constructive Sequential Games"
Journal of Economic Methodology, Vol. 24, Issue 4, 384-409.
Leigh Tesfatsion, Chris R. Rehmann, Diego S. Cardoso, Yu Jie, and William J. Gutowski (2017), "An Agent-Based Platform for the Study of Watersheds as Coupled Natural and Human Systems"
Environmental Modelling & Software, Vol. 89, March,
- Ekaterina Sinitskaya and Leigh Tesfatsion (2015), "Macroeconomies as Constructively Rational Games"
Journal of Economic Dynamics and Control, Vol. 61, December, 152-182.
Paul Borrill and Leigh Tesfatsion (2011), "Agent-Based Modeling: The Right Mathematics for the Social Sciences?"
pages 228-258 in J.B. Davis and D.W. Hands (eds.), Elgar Companion to Recent Economic Methodology, Edward Elgar Publishers, 560pp. ISBN-13: 9781848447547
Hongyan Li and Leigh Tesfatsion (2011), "ISO Net Surplus Collection and Allocation in Wholesale Power Markets Under Locational Marginal Pricing"
IEEE Transactions on Power Systems, Vol. 26, No. 2, 627-641.
- Blake LeBaron and Leigh Tesfatsion (2008), "Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents"
American Economic Review (Papers & Proceedings), Vol. 98, No. 2, 246-250.
- Leigh Tesfatsion (2006), Agent-Based Computational Economics: A Constructive Approach to Economic Theory
Introductory chapter (pp. 831-880) in Leigh Tesfatsion and Kenneth L. Judd (eds.),
Handbook of Computational Economics 2: Agent-Based
(Contributors and Contents),
Handbooks in Economics Series, North-Holland (Elsevier), Amsterdam, the Netherlands.
- Leigh Tesfatsion (2002), "Economic Agents and Markets as Emergent Phenomena"
Proceedings of the National Academy of Sciences U.S.A., Vol. 99, Supp. 3, 7191-7192.
- Leigh Tesfatsion (2001), "Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search"
Journal of Economic Dynamics and Control, Vol. 25, Nos. 3-4, 419-457.
- Mark Pingle and Leigh Tesfatsion (1998), "Active Intermediation in a Monetary Overlapping Generations Economy"
Journal of Economic Dynamics and Control, Vol. 22, 1533-1574.
- Leigh Tesfatsion (1997), "A Trade Network Game with Endogenous Partner Selection"
pages 249-269 in H. M. Amman, B. Rustem, and A. B. Whinston
(eds.), Computational Approaches to Economic Problems, Kluwer Academic Publishers.
- Robert E. Kalaba and Leigh Tesfatsion (1996), "A Multicriteria Approach to Model Specification and Estimation"
Computational Statistics and Data Analysis, Vol. 21, 193-214 (lead article).
- Robert E. Kalaba and Leigh Tesfatsion (1990), "Flexible Least Squares for Approximately Linear Systems"
IEEE Transactions on Systems, Man, and Cybernetics,
Vol. 20, No. 5, 978-989.
- Robert E. Kalaba and Leigh Tesfatsion (1990), "Nonlocal Automated Sensitivity Analysis"
Computers and Mathematics with Applications, Vol. 20, No. 2, 53-65.
- Robert E. Kalaba and Leigh Tesfatsion (1981), "An Exact Sequential Solution Procedure for a Class of Discrete-Time Nonlinear Estimation Problems"
IEEE Transactions on Automatic Control, Vol. AC-26, 1133-1149.
Leigh Tesfatsion (1979), "Direct Updating of Intertemporal Criterion Functions for a Class of Adaptive Control Problems"
IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-9, 143-151.