no code implementations • 13 Oct 2023 • Fabio Maccheroni, Massimo Marinacci, Ruodu Wang, Qinyu Wu
We then extend the analysis to comparative risk aversion by showing that the notion of Yaari (1969) corresponds to comparative propension to full insurance, while the stronger notion of Ross (1981) corresponds to comparative propension to partial insurance.
no code implementations • 5 May 2023 • Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
We develop a full-fledged analysis of an algorithmic decision process that, in a multialternative choice problem, produces computable choice probabilities and expected decision times.
no code implementations • 13 Apr 2023 • Massimo Marinacci, Giulio Principi, Lorenzo Stanca
We illustrate the strong implications of recursivity, a standard assumption in dynamic environments, on attitudes toward uncertainty.
no code implementations • 1 Aug 2020 • Carlo Baldassi, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Methods for the solution of NP-hard optimization problems in which the cost function is known.
no code implementations • 1 Aug 2020 • Simone Cerreia-Vioglio, Lars Peter Hansen, Fabio Maccheroni, Massimo Marinacci
We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations."
no code implementations • 17 Jul 2020 • Simone Cerreia-Vioglio, Per Olov Lindberg, Fabio Maccheroni, Massimo Marinacci, Aldo Rustichini
We prove that a random choice rule satisfies Luce's Choice Axiom if and only if its support is a choice correspondence that satisfies the Weak Axiom of Revealed Preference, thus it consists of alternatives that are optimal according to some preference, and random choice then occurs according to a tie breaking among such alternatives that satisfies Renyi's Conditioning Axiom.
no code implementations • 3 May 2020 • Carlo Baldassi, Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci, Marco Pirazzini
We introduce an algorithmic decision process for multialternative choice that combines binary comparisons and Markovian exploration.
no code implementations • 28 Apr 2020 • Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci, Aldo Rustichini
We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation \[ p_{t}\left( a, A\right) =\dfrac{e^{\frac{u\left( a\right) }{\lambda \left( t\right) }+\alpha \left( a\right) }}{\sum_{b\in A}e^{\frac{u\left( b\right) }{\lambda \left( t\right) }+\alpha \left( b\right) }}% \] where $p_{t}\left( a, A\right) $ is the probability that alternative $a$ is selected from the set $A$ of feasible alternatives if $t$ is the time available to decide, $\lambda$ is a time dependent noise parameter measuring the unit cost of information, $u$ is a time independent utility function, and $\alpha$ is an alternative-specific bias that determines the initial choice probabilities reflecting prior information and memory anchoring.