Weighting of Small Probabilities and Its Market Applications

2008 ◽  
Vol 40 (2) ◽  
pp. 210-218 ◽  
Author(s):  
Rong CHEN
Keyword(s):  
Small Probabilities

Argument from Chance

2015 ◽  
Vol 7 (1) ◽  
pp. 199-207 ◽  
Author(s):  
Dariusz Łukasiewicz
Keyword(s):  
Evidential Argument ◽  
Theistic Argument ◽  
Starting Point ◽  
Argument From Evil ◽  
The Universe ◽  
Small Probabilities

In the article, first I present the atheistic argument from pointless evil and the argument from chance. The essence of the argument from chance consists in the incompatibility of the existence of purposeless events and the existence of a God who planned the universe to the last detail. Second, I would like to show that there is a relation between the evidential argument from evil and the argument from chance. An analysis of the theistic argument from small probabilities is a helpful starting point for the presentation of how the two arguments are related.

scholarly journals The Nature of Risk Preferences: Evidence from Insurance Choices

2013 ◽  
Vol 103 (6) ◽  
pp. 2499-2529 ◽  
Author(s):  
Levon Barseghyan ◽  
Francesca Molinari ◽  
Ted O'Donoghue ◽  
Joshua C Teitelbaum
Keyword(s):  
Loss Aversion ◽  
Structural Model ◽  
Marginal Utility ◽  
Unobserved Heterogeneity ◽  
Risky Choice ◽  
Probability Weighting ◽  
Disappointment Aversion ◽  
Diminishing Marginal Utility ◽  
Standard Risk ◽  
Small Probabilities

We use data on insurance deductible choices to estimate a structural model of risky choice that incorporates “standard” risk aversion (diminishing marginal utility for wealth) and probability distortions. We find that probability distortions—characterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes—play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. We demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting. (JEL D14, D81, G22)

Matrix Madness

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Daniel Kleitman
Keyword(s):  
Computer Simulation ◽  
Small Probabilities

Considering infinitesimally-small probabilities, computer simulacra, and synapse firings, Daniel Kleitman marshals some mathematical objections to Nick Bostrom’s well-known paper, “Are You Living in a Computer Simulation?”

Pricing discretely-monitored double barrier options with small probabilities of execution

2021 ◽  
Vol 290 (1) ◽  
pp. 313-330 ◽  
Author(s):  
Vasileios E. Kontosakos ◽  
Keegan Mendonca ◽  
Athanasios A. Pantelous ◽  
Konstantin M. Zuev
Keyword(s):  
Barrier Options ◽  
Double Barrier ◽  
Small Probabilities

The Design Inference: Eliminating Chance through Small Probabilities

2000 ◽  
Vol 2 (1) ◽  
pp. 142-144
Author(s):  
Ray Bohlin ◽  
Keyword(s):  
Small Probabilities

Feedback Produces Divergence From Prospect Theory in Descriptive Choice

2008 ◽  
Vol 19 (10) ◽  
pp. 1015-1022 ◽  
Author(s):  
Ryan K. Jessup ◽  
Anthony J. Bishara ◽  
Jerome R. Busemeyer
Keyword(s):  
Model Comparison ◽  
Objective Probability ◽  
Decision Making Process ◽  
Probability Weighting ◽  
Critical Component ◽  
Gambling Task ◽  
Individual Subject ◽  
Decision Weights ◽  
The Individual ◽  
Small Probabilities

A recent study demonstrated that individuals making experience-based choices underweight small probabilities, in contrast to the overweighting observed in a typical descriptive paradigm. We tested whether trial-by-trial feedback in a repeated descriptive paradigm would engender choices more correspondent with experiential or descriptive paradigms. The results of a repeated gambling task indicated that individuals receiving feedback underweighted small probabilities, relative to their no-feedback counterparts. These results implicate feedback as a critical component during the decision-making process, even in the presence of fully specified descriptive information. A model comparison at the individual-subject level suggested that feedback drove individuals' decision weights toward objective probability weighting.

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