The Seller's Sense: Buying-Selling Perspective Affects the Sensitivity to Expected Value Differences

2016 ◽  
Vol 30 (2) ◽  
pp. 197-208 ◽  
Author(s):  
Eldad Yechiam ◽  
Taher Abofol ◽  
Thorsten Pachur
Keyword(s):  
Expected Value ◽  
Value Differences

scholarly journals Regret in Experience-Based Decisions: The Effects of Expected Value Differences and Mixed Gains and Losses

2020 ◽  
Author(s):  
William M. Hayes ◽  
Douglas Wedell
Keyword(s):  
Reinforcement Learning ◽  
Structural Features ◽  
Expected Value ◽  
Online Experiment ◽  
Value Differences ◽  
Mixed Outcomes ◽  
Relative Comparisons ◽  
Gains And Losses ◽  
The Impact

Previous research on experience-based decisions with full feedback supports the idea that people tend to prefer options that minimize the probability of regret. The current study explored whether this preference is modulated by differences in expected value (EV) and the presence or absence of occasional losses. Participants (n = 52) completed an online experiment that involved repeated choices between a safer and a riskier option while receiving full feedback. The riskier option yielded a better outcome on 80% of draws so that choosing it minimized the probability of regret. Preference for the riskier, regret-minimizing option was high when it had the same EV as the safer option and all outcomes were gains, but it decreased when the safer option had a higher EV and when both options included occasional losses. Outcome ratings that were obtained on 50% of trials showed large effects of regret and rejoicing, confirming that participants were sensitive to relative comparisons between obtained and forgone outcomes. Reinforcement-learning modeling indicated that the effects of unequal EVs and mixed outcomes could be accounted for by assuming combined encoding of absolute and relative outcomes and unequal weighting of gains and losses. Overall, these results demonstrate that the impact of regret can be modulated by structural features of the choice environment.

The Expected Value Premium

CFA Digest ◽  
2008 ◽  
Vol 38 (3) ◽  
pp. 35-36
Author(s):  
Michael Kobal
Keyword(s):  
Expected Value ◽  
Value Premium

First-Year Engineering Computing Courses in Canadian Engineering Programs

1969 ◽  
Author(s):  
Sean Maw ◽  
Janice Miller Young ◽  
Alexis Morris
Keyword(s):  
Problem Solving ◽  
Engineering Students ◽  
Expected Value ◽  
Knowledge Development ◽  
First Year ◽  
Recent Past ◽  
Engineering Programs ◽  
Digital Computation ◽  
Pedagogical Philosophies ◽  
Introductory Computing

Most Canadian engineering students take a computing course in their first year that introduces them to digital computation. The Canadian Engineering Accreditation Board does not specify the language(s) that can or should be used for instruction. As a result, a variety of languages are used across Canada. This study examines which languages are used in degree-granting institutions, currently and in the recent past. It also examines why institutions have chosen the languages that they currently use. In addition to the language used in instruction, the types and hours of instruction are also analyzed. Methods of instruction and evaluation are compared, as well as the pedagogical philosophies of the different programs with respect to introductory computing. Finally, a comparison of the expected value of this course to graduates is also presented. We found a more diverse landscape for introductory computing courses than anticipated, in most respects. The guiding ethos at most institutions is skill and knowledge development, especially around problem solving in an engineering context. The methods to achieve this are quite varied, and so are the languages employed in such courses. Most programs currently use C/C++, Matlab, VB and/or Python.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

Political identity over personal impact: Early US reactions to the COVID-19 pandemic

2020 ◽  
Author(s):  
Robert N Collins ◽  
David R. Mandel ◽  
Sarah S. Schywiola
Keyword(s):  
Emotional Distress ◽  
Strong Influence ◽  
Political Identity ◽  
Organizational Response ◽  
Threat Perception ◽  
Attitudes And Beliefs ◽  
Value Differences ◽  
Personal Impact ◽  
Large N ◽  
Weak Influence

Research suggests political identity has strong influence over individuals’ attitudes and beliefs, which in turn can affect their behavior. Likewise, firsthand experience with an issue can also affect attitudes and beliefs. A large (N = 10,362) survey (Pew Research and Ipsos W64) of Americans was analyzed to investigate the effects of both political identity and personal impact on individuals’ reactions to the COVID-19 pandemic. Results show that political identity (i.e., Democrat or Republican) and personal impact (i.e., personally affected or not) influenced different aspects of the American public’s reaction to COVID-19. Political identity exerted a strong influence on self-reports of emotional distress, threat perception, discomfort with exposure, support for restrictions, and perception of under/overreaction by individuals and institutions. Personal impact exerted a comparatively weaker influence on reported emotional distress and threat perception. Both factors had a weak influence on appraisal of individual and organizational and community responses. The dominating influence of political identity carried over into the bivariate relations among these responses. In particular, the appraisal of organizational response divided along party lines, tied to opposing views of whether there has been over- or under-reaction to the pandemic. The dominance of political identity has important implications for crisis management and reflects the influence of normative value differences between the parties, partisan messaging on the pandemic, and polarization in American politics.

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