PRIN 2022 - MALERP

Increasing attention has been devoted to the problem of aggregating the diffuse opinions of a large number of agents into a unique prediction. This general idea goes by the name of “Wisdom of Crowds” (WOC) and it has been applied to many contexts, from guiding investment decisions, generating new business ideas, predicting political or financial events, to forming a consensus about climate change. Various models have been proposed to capture the WOC, and empirical evidence shows that crowd consensus can potentially deliver accurate predictions. However, these predictions can be unreliable

because their accuracy depends on strong assumptions on the distribution of agents’ opinions and these procedures are not robust to agents’ strategic manipulation. The goal of this project is to propose a new aggregation mechanism called "Generalized Market Predictions” (GMP) that improves upon alternative ways to harness the WOC. GMP is inspired by the literature on Financial Economics, General Equilibrium, Computer Science, and Experimental Economics. It is a flexible theory for aggregating agents’ opinions in a consensus which is reliable and efficient. These two properties are not guaranteed by other aggregators of public opinions. GMP is guaranteed to be reliable and efficient because of two innovations. First, GMP recommends informing agents about a tentative crowd consensus before they express their final opinion. Second, the consensus is calculated by weighting the opinions of different agents taking into account their past performance. Our theoretical work will deliver different candidates on how to construct a consensus from prices and carefully study the resulting accuracy. We will start from a general equilibrium setting and gradually move away from it. We will first consider temporary equilibrium and introduce a stochastic clock, such that to mimic agents interactions in continuous time. Then, we plan to abandon the Walrasian assumption of the price formation mechanism and study the performance of general aggregators of opinions. The richness of our approach will also allow us to contribute to the ongoing debate on the recovery of the ``true’’ probability implied by market prices – the most natural aggregators of market participants' opinions. The final step of this project is to bring our theoretical results to the real world. We will compile instructions on how to design a market mechanism which can be adopted by an institution in many domains, such as improving prediction of emerging local and regional health threats, improving accuracy in predicting the impact of environmental changes caused by climate and local factors, and improving the efficiency of firms. To test the effectiveness of our design, we will conduct an experimental study involving real subjects.

In particular, our experiment will evaluate the performance of GMP in a comparative manner, carefully calibrating the trade-off between accuracy and convergence rate. 

ENTE PROMOTORE: Unione Europea - MUR

NOME PROGETTO: PRIN 2022 Market Learning and Robust Predictions (MALERP); COD. MUR: 20228XTY79

PERIODO E DURATA: 14/10/2023 – 13/10/2025

FINANZIAMENTO: Missione 4 “Istruzione e Ricerca” del Piano Nazionale di Ripresa e Resilienza ed in particolare la componente C2 – investimento 1.1, Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN) – del Piano Nazionale di Ripresa e Resilienza, dedicata ai Progetti di ricerca di Rilevante Interesse Nazionale - CUP: J53D23004190006

COORDINATORE: Alma Mater Studiorum, Università degli studi di BOLOGNA

REFERENTI SSSA: Prof. Daniele Giachini