Professor of Economics
3217 Kellogg Global Hub, Department of Economics
Northwestern University
Phone: (847) 491-5398
email: marciano AT northwestern DOT edu
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Marciano Siniscalchi
Professor of Economics 3217 Kellogg Global Hub, Department of Economics Northwestern University Phone: (847) 491-5398 email: marciano AT northwestern DOT edu |
Structural Rationality in Dynamic games; Econometrica, vol. 90 n.5, September 2022.
Risk Sharing in the Small and in the Large, with Paolo Ghirardato; Journal of Economic Theory, vol. 175, May 2018. Online Appendix.
Lexicographic Beliefs and Assumption, with Eddie Dekel and Amanda Friedenberg; Journal of Economic Theory, vol. 163, May 2016. Working-paper version (updated).
Ambiguity in the small and in the large (former title: "A more robust definition of multiple priors"). Econometrica, vol. 80 n. 6, November 2012.
Supplemental appendix with an axiomatization of locally Lipschitz and "nice" preferences, plus additional results.
Previous version with additional material and extensive calculations for specific decision models.
Dynamic Choice under Ambiguity, Theoretical Economics, vol. 6 n.3, September 2011.
Two out of three ain't bad: a comment on 'The ambiguity aversion literature: A critical assessment', Economics and Philosophy, Vol. 25 n.3, 2009.
Vector Expected Utility and Attitudes toward Variation, Econometrica, Vol. 77 n. 3, May 2009. Supplementary material. Also see the additional material below (under Manuscripts).
Parental Guidance and Supervised Learning, with Alessandro Lizzeri. Quarterly Journal of Economics, vol. 123 n. 3, August 2008. The working-paper version has additional material, so we are keeping it available for download.
Interactive epistemology in games with payoff uncertainty, with Pierpaolo Battigalli. Research in Economics, vol. 61, 2007.
A Behavioral Characterization of Plausible
Priors, Journal of Economic Theory vol. 128, 2006. See also the Online Appendix for additional results and omitted proofs.
Efficient Sorting in a Dynamic Adverse-Selection Model, with Igal Hendel and Alessandro Lizzeri. Review of Economic Studies vol. 72 n. 2, April 2005. See also the Web Appendix for additional results and omitted proofs.
A Subjective Spin on Roulette Wheels, with Paolo Ghirardato, Fabio Maccheroni and Massimo Marinacci; Econometrica , vol. 71 n. 6, November 2003.
Rationalization and Incomplete Information, with Pierpaolo Battigalli. Advances in Theoretical Economics, Vol. 3 No. 1, Article 3. BEPRess link: http://www.bepress.com/bejte/advances/vol3/iss1/art3.
Rationalizable Bidding in First-Price Auctions, with Pierpaolo Battigalli. Games and Economic Behavior, 45, October 2003, pp. 38-72.
Strong Belief and Forward-Induction Reasoning, with Pierpaolo Battigalli; Journal of Economic Theory (2002), 106 no. 2, pp. 356-391.
Hierarchies of Conditional Beliefs and Interactive Epistemology in Dynamic games, with Pierpaolo Battigalli. Journal of Economic Theory (1999), 88, 188-230. Additional material not in the published version.
Interactive Beliefs, Epistemic Independence and Strong Rationalizability, with Pierpaolo Battigalli. Research in Economics (1999) 53, 247-273.
Human or Machine? Assessing AI's Ability to Generate Game Theory Problems, with Ben Golub and Annie Liang; February 2026.
Abstract.
AI models now excel at solving difficult applied mathematics problems; we ask
how well they can compose such problems, focusing on undergraduate game
theory. Adapting the Turing (1950) test to problem generation, we collect
problems from professors and GPT-5, standardizing presentation so evaluation
focuses on content rather than style. Sixty-seven experts — undergraduate and
graduate students who have taken game theory — classify problems as human or LLM-generated.
We find that AI output is indistinguishable to any single evaluator yet different in aggregate.
Individually, evaluators perform at chance (mean accuracy 50.9%). However, pooling 2,680 classifications rejects the null
that the two distributions are identical (p = 0.014). The signal resides in
solutions, not problem statements: restricting to evaluators who observe solutions and report medium or high confidence, pooled accuracy rises to 53.4%
(p = 0.0006), while without solutions we cannot reject the null. We train a
classifier to distinguish the problem sources; the strongest objective feature
separating human problems is the solution word count to problem word count
ratio: human-authored problems tend to require more reasoning per unit of
setup. We discuss implications of our findings for organizations that delegate
knowledge work to AI.
Self-Image Bias and Lost Talent, with Pietro Veronesi; August 2023.
Online Appendix
Abstract. We propose an overlapping-generation model wherein researchers belong to
two groups, M or F, and established researchers evaluate new researchers. Group
imbalance obtains even with group-neutral evaluations and identical productivity
distributions. Evaluators' self-image bias and mild between-group heterogeneity
in equally productive research characteristics lead the initially dominant group,
say M, to promote scholars similar to them. Promoted F-researchers are few
and similar to M-researchers, perpetuating imbalance. Consistently with the
data, our mechanism also predicts stronger and widening group imbalance in top
institutions; higher quality of accepted F-researchers; clustering of M- and F-
researchers across different fields; greater imbalance for seniors than juniors; less
credit for F-researchers in co-authored work; and established researchers' false
perception that increasing F-representation reduces quality. Policy-wise, men-
torship reduces group imbalance, but increases F-group talent loss. Affirmative
action reduces both.
Foundations for Structural Preferences; May 2020. New version coming soon!
Abstract. The analysis of key game-theoretic concepts such as sequential rationality or backward- and forward-induction hinges on assumptions about players' actions and beliefs at information sets that are not actually reached during game play, and that players themselves do not expect to reach. However, it is not obvious how to elicit intended actions and conditional beliefs at such information sets. In Siniscalchi (2018), I address this concern by introducing a novel optimality criterion, \emph{structural rationality}, which implies sequential rationality but allows for the incentive-compatible elicitation of beliefs and intended actions. The present paper complements the analysis by providing an axiomatic foundation for structural preferences.
Note: not quite polished yet, but the main material and all results are there.
Previous versions of the Structural Rationality paper: Structural Rationality in Dynamic games; October 2021. Structural Rationality in Dynamic Games, May 2018. Structural Rationality in Dynamic Games; May 2020. Online Appendix of the 2020 version.
Recursive Vector Expected Utility; May 2010
Abstract.
This paper proposes and axiomatizes a recursive version of the vector expected utility (VEU) decision model (Siniscalchi, 2009). Recursive VEU preferences are dynamically consistent and ``consequentialist.'' Dynamic consistency implies standard Bayesian updating of the baseline (reference) prior in the VEU representation, but imposes no constraint on the adjustment functions and one-step-ahead adjustment factors. This delivers both tractability and flexibility.
Recursive VEU preferences are also consistent with a dynamic, i.e. intertemporal extension of atemporal VEU preferences. Dynamic consistency is characterized by a time-separability property of adjustments---the VEU counterpart of Epstein and Schneider (2003)'s rectangularity for multiple priors.
A simple exchangeability axiom ensures that the baseline prior admits a representation a la de Finetti, as an integral of i.i.d. product measures with respect to a unique probability µ. Jointly with dynamic consistency, the same axiom also implies that µ is updated via Bayes' Rule to provide an analogous representation of baseline posteriors.
Finally, an application to a dynamic economy a la Lucas (1978) is sketched.
Additional Material for Vector Expected Utility...
December 2007 version; contains an alternative formulation and additional results.
Machina's Reflection Example and VEU Preferences: a Very Short Note. Shows that VEU preferences that are ambiguity-averse in the sense of Ghirardato and Marinacci (2002), but not in the sense of Schemidler (1989), can accommodate Machina's now-famous example.
Bayesian Updating for General Maxmin-Expected Utility Preferences, September 2001. The main result of this paper has been incorporated in Section 4 of ``Dynamic Choice under Ambiguity'', available above. So this paper is basically obsolete...
Epistemic Game Theory, January 2014; with Eddie Dekel. Forthcoming in the Handbook of Game Theory, vol. 4. See also Online appendix, with Eddie Dekel and Luciano Pomatto.
Ambiguity and Ambiguity Aversion, June 2013; with Mark Machina. In the Handbook of the Economics of Risk and Uncertianty, 2014.
Epistemic Game Theory: Beliefs and Types, March 2007. Marciano Siniscalchi, The New Palgrave Dictionary of Economics, forthcoming, Palgrave Macmillan, reproduced with permission of Palgrave Macmillan.
Ambiguity and Ambiguity Aversion, March 2005. Marciano Siniscalchi, The New Palgrave Dictionary of Economics, forthcoming, Palgrave Macmillan, reproduced with permission of Palgrave Macmillan.