Research Archive: Publications, Teaching Materials, & Working Papers.
This page presents my some of my published research, teaching materials, and current working papers.
My Publications
Exploratory behaviour across tasks and measures: On convergent validity, domain-generality, and temporal stability.
Anvari, F., S. Billinger, P. Analytis, V. R. Franco, & D. Marchiori (2024). Nature Communications, 15, 7721.
K-Alpha Calculator—Krippendorff's Alpha Calculator: A User-Friendly Tool for Computing Krippendorff's Alpha Inter-Rater Reliability Coefficient
Marzi, G., Balzano, M., & Marchiori, D. (2024). MethodsX, 102545.
Percent framing attenuates the magnitude effect in a preference-matching task of intertemporal choice.
Anvari, F., Verdeș, D. D., & Marchiori, D. (2022). PLOS ONE, 17(1), e0262620.
Priming exploration across domains: Does search in a spatial environment influence search in a cognitive environment?
Anvari, Farid, & Davide Marchiori (2021). Royal Society Open Science, 8:201944.
Plasticity of human strategic sophistication in interactive decision-making.
Marchiori, Davide, Sibilla Di Guida, & Luca Polonio (2021). Journal of Economic Theory, 196:105291.
Hierarchical decision-making produces persistent differences in learning performance.
Knudsen, Thorbjørn, Davide Marchiori, & Massimo Warglien (2018). Scientific Reports, 8:15782.
Cross Cultural Differences in Decisions from Experience: Evidence from Denmark, Israel, and Taiwan.
Di Guida, Sibilla, Ido Erev, & Davide Marchiori (2015). Journal of Economic Psychology, 49:47-58.
Toward a general theoretical framework for judgment and decision-making.
Marchiori, Davide, & Itzhak Aharon (2015). Frontiers in Psychology, 6:159.
Noisy retrieval models of over- and under-sensitivity to rare events.
Marchiori, Davide, Sibilla Di Guida, & Ido Erev (2015). Decision 2: 82-106.
On Loss Aversion, Level-1 Reasoning, and Betting.
Erev, Ido, Sharon Gilat-Yihyie, Davide Marchiori, & Doron Sonsino (2015). International Journal of Game Theory, 44:113-133.
Pack light if on the move: exploitation and exploration in a dynamic environment.
LiCalzi, Marco, & Davide Marchiori (2014). In: S. Leitner & F. Wall (Eds.), Artificial Economics and Self Organization (pp. 205–216). Springer International Publishing
Decisions Among Defaults and the Effect of the Option to do Nothing.
Di Guida, Sibilla, Davide Marchiori, & Ido Erev (2012). Economics Letters, 117:790-793.
Physiological Plausibility and Boundary Conditions of Theories of Risk Sensitivity.
Marchiori, Davide, & Shira Elqayam (2012). Frontiers in Psychology, 3:33.
Neural Network Models of Learning and Categorization in Multigame Experiments.
Marchiori, Davide, & Massimo Warglien (2011). Frontiers in Neuroscience, 5:139.
Predicting Human Behavior by Regret Driven Neural Networks.
Marchiori, Davide, & Massimo Warglien (2008). Science, 319:1111-1113.
Constructing shared interpretations in a team of intelligent agents: the effects of communication intensity and structure.
Marchiori, Davide, & Massimo Warglien (2005). In: T. Terano, H. Kita, T. Kaneda, K. Arai, & H. Deguchi (Eds.), Agent-Based Simulation: From Modeling Methodologies to Real-World Applications (pp. 58–71). Springer Tokyo.
My Teaching Materials
Experimental Methods for Management decisions
In today's business environment, managers often face decisions under uncertainty, from launching new products to implementing business models. Rather than relying on intuition or outdated information, this course teaches a scientific approach to decision-making. Students learn how to design experiments, collect high-quality data, and use statistical analysis to make better management decisions. The course covers experimental design principles, hypothesis testing, and data analysis tools specifically tailored for business contexts. Practical applications include market testing, operational improvements, and strategic planning. Additionally, students develop skills in scientific communication valuable for both academic work and business presentations.
Advanced Management Principles: Negotiation
This course explores the theory and practice of negotiation, blending decision analysis frameworks with strategic game theory. Students learn to assess key negotiation elements including interests, alternatives, and power dynamics while developing practical skills in both distributive and integrative bargaining. The course emphasizes principled negotiation approaches that create sustainable agreements through objective criteria and mutual gains, while examining how negotiator behavior and relationships influence outcomes. Through theoretical models and real-world applications, students develop a comprehensive understanding of effective negotiation strategies in various contexts.
My Working Papers
Distracting from Equilibrium: How Feedback Can Reinforce Heuristic Play in Strategic Games
Di Guida, S., D. Marchiori, D. Mayeaux, & L. Polonio (2024). Under review.