Risk-Corrected Probabilities of a Binary Event

This chapter provides a brief survey of gravitational-wave astronomy, including the recent recent breakthrough detection. It sets the stage for the rest of the book via simple back-of-the-envelope estimates for different sets of sources. The chapter also describes the first detection of a black hole merger (GW150914) as well as the first observed neutron star binary event (GW170817) and introduces some of the ideas required to understand these breakthroughs.

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Stand growth projection: simultaneous estimation of growth and mortality using a single probabilistic function

Canadian Journal of Forest Research ◽

10.1139/x87-225 ◽

1987 ◽

Vol 17 (11) ◽

pp. 1466-1470 ◽

Cited By ~ 5

Author(s):

K. E. Lowell ◽

R. J. Mitchell

Keyword(s):

Logistic Model ◽

Forest Growth ◽

Growth And Yield ◽

Diameter Distribution ◽

Simultaneous Estimation ◽

Binary Event ◽

Probabilistic Function ◽

Growth Projection ◽

Individual Trees ◽

Stand Characteristics

Logistic regression analysis can be used to estimate the probability of a binary event. In forestry, its use largely has been limited to predicting the probability of mortality of individual trees. However, the potential for broader application in forest growth and yield modelling has largely been overlooked. A logistic model to predict the probability that a tree will attain a specified future diameter can be produced by establishing a series of growth "success" criteria. Given the initial diameter distribution of a forest stand, a future diameter distribution and stand characteristics can be estimated probabilistically by estimating the proportion of stems in each diameter class of the distribution which attains a specified future diameter (the "success" criterion) and the proportion which fails to achieve at least zero growth (i.e., mortality). Using permanent plot data, such a logistic model was calibrated and validated for an oak–hickory forest in southeastern Missouri. Validation indicated that the model performs satisfactorily (estimates are unbiased) for individual trees over a 5-year prediction period, and for stand characteristics over 5-, 10-, 15-, and 20-year prediction periods though precision suffers as prediction period lengthens.

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Betting, Selection, and Luck: A Long-Run Analysis of Repeated Betting Markets

Entropy ◽

10.3390/e21060585 ◽

2019 ◽

Vol 21 (6) ◽

pp. 585 ◽

Cited By ~ 1

Author(s):

Giulio Bottazzi ◽

Daniele Giachini

Keyword(s):

Relative Risk ◽

The Other ◽

Risk Averse ◽

Quantal Response ◽

Two Agents ◽

Long Run ◽

Binary Event ◽

Betting Market ◽

Betting Markets ◽

The Difference

We consider a repeated betting market populated by two agents who wage on a binary event according to generic betting strategies. We derive new simple criteria, based on the difference of relative entropies, to establish the relative wealth of the two agents in the long-run. Little information about agents’ behavior is needed to apply the criteria: it is sufficient to know the odds traders believe fair and how much they would bet when the odds are equal to the ones the other agent believes fair. Using our criteria, we show that for a large class of betting strategies, it is generically possible that the ultimate winner is only decided by luck. As an example, we apply our conditions to the case of Constant Relative Risk Averse (CRRA) and quantal response betting.

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A Prediction Market-Based Gamified Approach to Enhance Knowledge Sharing in Organizations

Simulation & Gaming ◽

10.1177/1046878119867382 ◽

2019 ◽

Vol 50 (5) ◽

pp. 572-597 ◽

Cited By ~ 3

Author(s):

Hajime Mizuyama ◽

Seiyu Yamaguchi ◽

Mizuho Sato

Keyword(s):

Knowledge Sharing ◽

Web Application ◽

Evaluation System ◽

Future Research ◽

Collective Knowledge ◽

Prediction Market ◽

Binary Event ◽

Private Knowledge ◽

Future Research Directions

Background. Knowledge sharing among the members of an organization is crucial for enhancing the organization’s performance. However, knowing how to motivate and direct members to effectively and efficiently share their relevant private knowledge concerning the organization’s activities is not entirely a straightforward matter. Aim. This study aims to propose a gamified approach not only for motivating truthful sharing and collective evaluation of knowledge among the members of an organization but also for properly directing those actions so as to maximize the usefulness of the shared knowledge. A case study is also conducted to understand how the proposed approach works in a live business scenario. Method. A prediction market game on a binary event on whether the specified activity will be completed successfully is devised. The game utilizes an original comment aggregation and evaluation system through which relevant knowledge can be shared verbally and evaluated collectively by the players themselves. Players’ behavior is driven toward a desirable direction with the associated incentive framework realized by three game scores. Results. The proposed gamified approach was implemented as a web application and verified with a laboratory experiment. The game was also played by four participants who deliberated on an actual sales proposal in a real company. It was observed that the various valuable knowledge elements that were successfully collected from the participants could be utilized for refining the sales proposal. Conclusions. The game induced motivation through gamification, and some of the designed game scores worked in directing the players’ behavior as desired. The players learned from others’ comments, which brought about a snowball effect and enriched collective knowledge. Future research directions include how to transform this knowledge into an easy-to-comprehend representation.

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