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1. Peer review: Risk and risk tolerance

   https://doi.org/10.1371/journal.pone.0273813  

   Gallo, Stephen A.; Schmaling, Karen B. (August 2022, PLOS ONE)

   Lam, Hon-Ming (Ed.)

   Peer review, commonly used in grant funding decisions, relies on scientists’ ability to evaluate research proposals’ quality. Such judgments are sometimes beyond reviewers’ discriminatory power and could lead to a reliance on subjective biases, including preferences for lower risk, incremental projects. However, peer reviewers’ risk tolerance has not been well studied. We conducted a cross-sectional experiment of peer reviewers’ evaluations of mock primary reviewers’ comments in which the level and sources of risks and weaknesses were manipulated. Here we show that proposal risks more strongly predicted reviewers’ scores than proposal strengths based on mock proposal evaluations. Risk tolerance was not predictive of scores but reviewer scoring leniency was predictive of overall and criteria scores. The evaluation of risks dominates reviewers’ evaluation of research proposals and is a source of inter-reviewer variability. These results suggest that reviewer scoring variability may be attributed to the interpretation of proposal risks, and could benefit from intervention to improve the reliability of reviews. Additionally, the valuation of risk drives proposal evaluations and may reduce the chances that risky, but highly impactful science, is supported. 

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2. Background Risk and Small-Stakes Risk Aversion

   https://doi.org/10.1257/aeri.20220480  

   Mu, Xiaosheng; Pomatto, Luciano; Strack, Philipp; Tamuz, Omer (June 2024, American Economic Review: Insights)

   Building on Pomatto, Strack, and Tamuz (2020), we identify a tight condition for when background risk can induce first-order stochastic dominance. Using this condition, we show that under plausible levels of background risk, no theory of choice under risk can simultaneously satisfy the following three economic postulates: (i) decision-makers are risk averse over small gambles, (ii) their preferences respect stochastic dominance, and (iii) they account for background risk. This impossibility result applies to expected utility theory, prospect theory, rank-dependent utility, and many other models. (JEL D81, D91) 

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3. Excess risk bounds in robust empirical risk minimization

   https://doi.org/10.1093/imaiai/iaab004  

   Mathieu, Timothée; Minsker, Stanislav (April 2021, Information and Inference: A Journal of the IMA)

   Abstract This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empirical risk minimization is based on the fact that for a ‘well-behaved’ stochastic process $$\left \{ f(X), \ f\in \mathscr F\right \}$$ indexed by a class of functions $$f\in \mathscr F$$, averages $$\frac{1}{N}\sum _{j=1}^N f(X_j)$$ evaluated over a sample $$X_1,\ldots ,X_N$$ of i.i.d. copies of $$X$$ provide good approximation to the expectations $$\mathbb E f(X)$$, uniformly over large classes $$f\in \mathscr F$$. However, this might no longer be true if the marginal distributions of the process are heavy tailed or if the sample contains outliers. We propose a version of empirical risk minimization based on the idea of replacing sample averages by robust proxies of the expectations and obtain high-confidence bounds for the excess risk of resulting estimators. In particular, we show that the excess risk of robust estimators can converge to $$0$$ at fast rates with respect to the sample size $$N$$, referring to the rates faster than $$N^{-1/2}$$. We discuss implications of the main results to the linear and logistic regression problems and evaluate the numerical performance of proposed methods on simulated and real data. 

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4. COVID-19: Risk perception, risk communication, and behavioral intentions.

   https://doi.org/10.1037/xap0000398  

   Joslyn, Susan; Savelli, Sonia; Duarte, Horacio A.; Burgeno, Jessica; Qin, Chao; Han, Jee Hoon; Gulacsik, Gala (December 2021, Journal of Experimental Psychology: Applied)
5. Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk

   https://doi.org/10.1109/TAC.2021.3131149  

   Chapman, Margaret P.; Bonalli, Riccardo; Smith, Kevin M.; Yang, Insoon; Pavone, Marco; Tomlin, Claire J. (December 2022, IEEE Transactions on Automatic Control)