106 research outputs found
Dataset of "Informing, simulating experience, or both: A field experiment on phishing risks"
No full textThis is the dataset used in "Informing, simulating experience, or both: A field experiment on phishing risks"
Data set of "Falling and failing (to learn)"
No full textData set of "Falling and failing (to learn): Evidence from a Nation-Wide Cybersecurity Field Experiment with SMEs"Accepted for publication in the Journal of Economic Behavior and Organization Abstract:Prior experiences are crucial in shaping risk prevention behavior. Previous studies have shown that experiencing a simulated phishing attack (a ``phishing drill") reduces the likelihood of clicking on unsafe links and disclosing one's password. In a large field experiment involving 670 small and medium-sized enterprises (SMEs) and their 33,000 employees, we examined the impact of experience on individuals' ability to detect cyber-security threats, and whether this effect persisted over several months. We collected data at both the company and individual levels, including risk preference, time preference, and trust. Our findings indicate only a non-systematic, short-term effect of previous phishing emails on clicking behavior. A cluster of individuals with greater patience, trust, and risk seeking was more likely to click on phishing links in the first place but then also more likely to benefit from phishing drills.</p
AUTHOR CORRECTION - ERS International Congress 2019:highlights from Best Abstract awardees
Full text linkLorna E. Latimer, Marieke Duiverman, Mahmoud I. Abdel-Aziz, Gulser Caliskan, Sara M. Mensink-Bout, Alberto Mendoza-Valderrey, Aurelien Justet, Junichi Omura, Karthi Srikanthan, Jana De Brandt. Breathe 2019; 15: e143–e149. This article from the December 2019 issue of Breathe was published with an error in the name of one of the authors. The corrected author list is shown above. The article has been corrected and republished online.</p
A tailor-made test of intransitive choice
No full textThis paper reports a new test of intransitive choice using individual measurements of regret- and similarity-based intransitive models of choice under uncertainty. Our test is tailor-made and uses subject-specific stimuli. Despite these features, we observed only a few intransitivities. A possible explanation for the poor predictive performance of intransitive choice models
is that they only allow for interactions between acts. They exclude within-act interactions by retaining the assumption that
preferences are separable over states of nature. Prospect theory, which relaxes separability but retains transitivity, predicted choices better. Our data suggest that descriptively realistic models must allow for within-act interactions but may retain transitivity
Prudence with respect to ambiguity
Full text linkUnder expected utility, prudence is equivalent to a positive third derivative of utility and plays a crucial role in precautionary saving behaviour. Eeckhoudt and Schlesinger (2006) proposed behavioural definitions of prudence and of higher order risk preferences. The present article proposes a similar definition for prudence with respect to ambiguity, i.e. situations in which objective probabilities are not available. Implications for several ambiguity models are derived. Ambiguity prudence is implied by Hansen and Sargent's (2001) multiplier preferences, empirically correlates with financial behaviour and plays a key role in prevention behaviour
Guess what I think: Essays on the wisdom in meta-predictions
Full text linkPeople’s self-reported beliefs, judgments and experiences are highly subjective and can be unreliable. However, such information is also very valuable. Researchers would like to know the true motives of people to understand their behavior. Practitioners can use subjective data to make better decisions. Furthermore, subjective judgments are useful in forecasting. Previous work suggests that the “Wisdom of Crowds” is an effective solution for predicting uncertain quantities. This dissertation develops novel methods to elicit and aggregate subjective information effectively. All methods are based on the following idea: What people think about other people’s judgments (“meta-prediction”) is related to their own judgment on the matter. Chapter 2 proposes a new forecast aggregation algorithm that improves the “Wisdom of Crowds” on the likelihood of an event. Simple average of forecasts could be biased due to common information among the forecasters. The algorithm uses meta-predictions to remove a potential bias in the collective forecast. Chapter 3 presents another solution to the same problem. Forecasters are incentivized such that the collective forecast becomes unbiased. Chapter 4 develops an incentive mechanism to elicit subjective information truthfully. The incentives are based on people’s meta-predictions. Truth-telling participants expect higher rewards. This motivation can improve the quality of the self-reported information. Chapter 5 focuses again on the “Wisdom of Crowds.” The chapter proposes a new algorithm to transform the average probability forecast. The transformed forecasts are much closer to the true probability of the uncertain event. Each chapter in this dissertation introduces a new incentive mechanism or an algorithm. Therefore, this dissertation makes methodological contributions to the literature on elicitation and aggregation of subjective information. Furthermore, each chapter presents experimental results to demonstrate practical effectiveness. The findings suggest that the meta-predictions can be useful. It is also easy to collect meta-predictions in simple surveys. Thus, this dissertation motivates subsequent work that could use meta-predictions even more extensively
Bayesian markets to elicit private information
Full text linkFinancial markets reveal what investors think about the future, and prediction markets are used to forecast election results. Could markets also encourage people to reveal private information, such as subjective judgments (e.g., “Are you satisfied with your life?”) or unverifiable facts? This paper shows how to design such markets, called Bayesian markets. People trade an asset whose value represents the proportion of affirmative answers to a question. Their trading position then reveals their own answer to the question. The results of this paper are based on a Bayesian setup in which people use their private information (their “type”) as a signal. Hence, beliefs about others’ types are correlated with one’s own type. Bayesian markets transform this correlation into a mechanism that rewards truth telling. These markets avoid two complications of alternative methods: they need no knowledge of prior information and no elicitation of metabeliefs regarding others’ signals
Malakoff Humanis Survey - Mental health of VSE-SME managing directors
No full textThis project aims to address the issue of mental health among managing directors in very small enterprises (VSEs) and small and medium enterprises (SMEs) considering its significant impact on individual well-being and the overall productivity and survival of these businesses. Despite its importance, research on mental health of employees and managers of VSE-SMEs is still underdeveloped. Mental health problems can lead to consequences such as decreased productivity due to absenteeism or lack of concentration. Also, the interdependence between employee and managerial mental health and possible contagious nature of negative behaviors, can have negative effects on workplace culture. The stigma surrounding mental health in competitive, managerial and leadership environments may discourage individuals from seeking help or treatment, which can exacerbate the problem. Furthermore, prevention efforts are often perceived as certain present costs for uncertain future benefits, which give companies little incentive to use them.
This study aims to measure and analyze various aspects including the mental health status of managing directors, their perceptions of mental health issues in terms of cost and prevention, as well as the determinants influencing the demand for mental health support and prevention strategies. Additionally, the research will consider objective data related to company structure and sector to provide a comprehensive understanding of the impactful factors
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