Bibliography

Search on correlated landscapes in economics and organizations

Brownian-motion models of policy and technology search, and the experimentation literature around them.

• S. Callander (2008). A theory of policy expertise. Quarterly Journal of Political Science, 3(2):123–140. doi:10.1561/100.00007024.
• S. Callander (2011). Searching for good policies. American Political Science Review, 105(4):643–662. doi:10.1017/s0003055411000426.
• S. Callander (2011). Searching and learning by trial and error. American Economic Review, 101(6):2277–2308. doi:10.1257/aer.101.6.2277.
• S. Callander and P. Hummel (2014). Preemptive policy experimentation. Econometrica, 82(4):1509–1528.
• S. Callander and T. S. Clark (2017). Precedent and doctrine in a complicated world. American Political Science Review, 111(1):184–203. doi:10.1017/s0003055416000587.
• S. Callander and N. Matouschek (2019). The risk of failure: trial and error learning and long-run performance. American Economic Journal: Microeconomics, 11(1):44–78. doi:10.1257/mic.20160359.
• S. Callander and N. McCarty (2024). Agenda control under policy uncertainty. American Journal of Political Science, 68(1):210–226.
• S. Callander, N. S. Lambert, and N. Matouschek (forthcoming). Innovation and competition on a rugged technological landscape. American Economic Journal: Microeconomics, forthcoming.
• Y. C. Aybas and S. Callander (2025). Cheap talk in complex environments. Working paper.
• A. Bardhi (2024). Attributes: selective learning and influence. Econometrica, 92(2):311–353. doi:10.3982/ecta18355.
• A. Bardhi and S. Callander (2026). Learning in a correlated world. Annual Review of Economics, 18, forthcoming. doi:10.1146/annurev-economics-051624-072515. pdf.
• U. Garfagnini and B. Strulovici (2016). Social experimentation with interdependent and expanding technologies. Review of Economic Studies, 83(4):1579–1613. doi:10.1093/restud/rdw008.
• D. Cetemen, C. Urgun, and L. Yariv (2023). Collective progress: dynamics of exit waves. Journal of Political Economy, 131(9):2402–2450.
• C. Urgun and L. Yariv (2025). Contiguous search: exploration and ambition on uncharted terrain. Journal of Political Economy, 133.
• Y. F. Wong (2025). Forward-looking experimentation of correlated alternatives. Theoretical Economics, 20. doi:10.3982/te4960.
• C. Carnehl and J. Schneider (2025). A quest for knowledge. Econometrica, 93(2):623–659. doi:10.3982/ecta22144.
• C. Hodgson and G. Lewis (2025). You can lead a horse to water: spatial learning and path dependence in consumer search. Econometrica, 93(4):1299–1332.
• S. C. Ganz (2020). Hyperopic search: organizations learning about managers learning about strategies. Organization Science, 31(4):821–838. doi:10.1287/orsc.2019.1330.
• D. Glick and C. D. Myers (2015). Learning from others: an experimental test of Brownian motion uncertainty models. Journal of Theoretical Politics, 27(4):588–612. doi:10.1177/0951629814559723.
• S. Malladi (2025). Searching in the dark and learning where to look. Working paper. doi:10.2139/ssrn.4084113.
• M. Banchio and S. Malladi (2025). Rediscovery. Working paper, arXiv:2504.19761.
• E. Ries (2011). The Lean Startup. Crown Business, New York.
• A. Camuffo, A. Cordova, A. Gambardella, and C. Spina (2020). A scientific approach to entrepreneurial decision making: evidence from a randomized control trial. Management Science, 66(2):564–586. doi:10.1287/mnsc.2018.3249.
• A. Camuffo, A. Gambardella, D. Messinese, E. Novelli, E. Paolucci, and C. Spina (2024). A scientific approach to entrepreneurial decision making: large scale replication and extension. Strategic Management Journal, 45(6):1209–1237. doi:10.1002/smj.3580.
• J. C. Gittins (1979). Bandit Processes and Dynamic Allocation Indices. Journal of the Royal Statistical Society B, 41(2):148–177. doi:10.1111/j.2517-6161.1979.tb01068.x.
• M. L. Weitzman (1979). Optimal Search for the Best Alternative. Econometrica, 47(3):641–654. doi:10.2307/1910412.
• B. Jovanovic and R. Rob (1990). Long Waves and Short Waves: Growth Through Intensive and Extensive Search. Econometrica, 58(6):1391–1409. doi:10.2307/2938321.
• D. A. Levinthal (1997). Adaptation on Rugged Landscapes. Management Science, 43(7):934–950. doi:10.1287/mnsc.43.7.934.
• N. Klein and S. Rady (2011). Negatively Correlated Bandits. Review of Economic Studies, 78(2):693–732. doi:10.1093/restud/rdq025.
• S. Callander, N. S. Lambert and N. Matouschek (2021). The Power of Referential Advice. Journal of Political Economy, 129(11):3073–3140. doi:10.1086/715850.
• S. Callander and N. Matouschek (2022). The Novelty of Innovation: Competition, Disruption, and Antitrust Policy. Management Science, 68(1):37–51. doi:10.1287/mnsc.2021.4101.
• S. Malladi, A. Martínez-Marquina and I. Morozov (2025). Space Exploration. Working paper. pdf.

Bayesian and global optimization

Myopic and non-myopic acquisition on Gaussian-process and Wiener models, and optimization of Brownian paths.

• H. J. Kushner (1964). A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Basic Engineering, 86(1):97–106. doi:10.1115/1.3653121.
• J. Mockus, V. Tiesis, and A. Zilinskas (1978). The application of Bayesian methods for seeking the extremum. In Towards Global Optimization, volume 2, pages 117–129. North-Holland.
• J. M. Calvin (1997). Average performance of a class of adaptive algorithms for global optimization. Annals of Applied Probability, 7(3):711–730. doi:10.1214/aoap/1034801250.
• D. R. Jones, M. Schonlau, and W. J. Welch (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492. doi:10.1023/a:1008306431147.
• P. I. Frazier, W. B. Powell, and S. Dayanik (2009). The knowledge-gradient policy for correlated normal beliefs. INFORMS Journal on Computing, 21(4):599–613. doi:10.1287/ijoc.1080.0314.
• D. Ginsbourger and R. Le Riche (2010). Towards Gaussian process-based optimization with finite time horizon. In mODa 9 –- Advances in Model-Oriented Design and Analysis, pages 89–96. Physica-Verlag. doi:10.1007/978-3-7908-2410-0_12.
• N. Srinivas, A. Krause, S. Kakade, and M. Seeger (2010). Gaussian process optimization in the bandit setting: no regret and experimental design. In Proceedings of the 27th International Conference on Machine Learning.
• P. Hennig and C. J. Schuler (2012). Entropy search for information-efficient global optimization. Journal of Machine Learning Research, 13:1809–1837.
• J. Snoek, H. Larochelle, and R. P. Adams (2012). Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25.
• B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas (2016). Taking the human out of the loop: a review of Bayesian optimization. Proceedings of the IEEE, 104(1):148–175. doi:10.1109/jproc.2015.2494218.
• R. Lam, K. Willcox, and D. H. Wolpert (2016). Bayesian optimization with a finite budget: an approximate dynamic programming approach. In Advances in Neural Information Processing Systems 29.
• Z. Wang and S. Jegelka (2017). Max-value entropy search for efficient Bayesian optimization. In Proceedings of the 34th International Conference on Machine Learning.
• J.-B. Grill, M. Valko, and R. Munos (2018). Optimistic optimization of a Brownian. In Advances in Neural Information Processing Systems 31.
• J. Wu and P. I. Frazier (2019). Practical two-step lookahead Bayesian optimization. In Advances in Neural Information Processing Systems 32.
• S. Jiang, D. R. Jiang, M. Balandat, B. Karrer, J. R. Gardner, and R. Garnett (2020). Efficient nonmyopic Bayesian optimization via one-shot multi-step trees. In Advances in Neural Information Processing Systems 33.
• J. Grosse, C. Zhang, and P. Hennig (2023). Optimistic optimization of Gaussian process samples. Transactions on Machine Learning Research.
• C. E. Rasmussen and C. K. I. Williams (2006). Gaussian Processes for Machine Learning. MIT Press. doi:10.7551/mitpress/3206.001.0001.
• M. Mahsereci and P. Hennig (2017). Probabilistic line searches for stochastic optimization. Journal of Machine Learning Research, 18:1–59.
• P. Cotton (2021). HumpDay: pure Python derivative-free optimization and its application suite. https://github.com/microprediction/humpday.

Ornstein–Uhlenbeck landscapes across fields

Exponentially correlated landscapes in evolution, fitness landscapes and radio propagation.

• T. F. Hansen (1997). Stabilizing selection and the comparative analysis of adaptation. Evolution, 51(5):1341–1351. doi:10.2307/2411186.
• M. A. Butler and A. A. King (2004). Phylogenetic comparative analysis: a modeling approach for adaptive evolution. The American Naturalist, 164(6):683–695. doi:10.2307/3473229.
• E. Weinberger (1990). Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics, 63(5):325–336. doi:10.1007/bf00202749.
• M. Gudmundson (1991). Correlation model for shadow fading in mobile radio systems. Electronics Letters, 27(23):2145–2146. doi:10.1049/el:19911328.
• X. Feng, K. A. Nguyen, and Z. Luo (2024). WiFi RTT RSS dataset for indoor positioning. Zenodo. doi:10.5281/zenodo.11558192. doi:10.5281/zenodo.11558192.

Path extremes and Gaussian geometry

Brownian bridges, path-product matrices and totally positive Gaussian models behind the covariance geometry.

• A. Alabert and R. Caballero (2018). On the minimum of a conditioned Brownian bridge. Stochastic Models, 34(3):269–291. doi:10.1080/15326349.2018.1465435.
• C. R. Johnson and R. L. Smith (1999). Path product matrices. Linear and Multilinear Algebra, 46(3):177–191. doi:10.1080/03081089908818612.
• S. Lauritzen, C. Uhler, and P. Zwiernik (2019). Maximum likelihood estimation in Gaussian models under total positivity. The Annals of Statistics, 47(4):1835–1863. doi:10.1214/17-aos1668.