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Engineering: Making Hard Decisions Under Uncertainty
Published in John X. Wang, Decision Making Under Uncertainty, 2002
The various components of an engineering decision problem may be integrated into a formal layout in the form of a decision tree, consisting of the sequence of decisions: a list of feasible alternatives; the possible outcome associated with each alternative; the corresponding probability assignments; and monetary consequence and utility evaluations. In the other words, the decision tree integrates the relevant components of the decision analysis in a systematic manner suitable for analytical evaluation of the optimal alternative. Probability models of engineering analysis and design may be used to estimate the relative likelihood of the possible outcomes, and appropriate value or utility models evaluate the relative desirability of each consequence.
Basics of probability and statistics
Published in Amit Kumar Gorai, Snehamoy Chatterjee, Optimization Techniques and their Applications to Mine Systems, 2023
Amit Kumar Gorai, Snehamoy Chatterjee
In the above discussion, the discrete sample space was considered. But, the probability can also be determined for a continuous sample space. For a continuous sample space, the probability of occurrence is measured as a probability density function. The probability density function of any continuous random variable gives the relative likelihood of any outcome in a specific range. Therefore, for a continuous random variable, the probability of an outcome of any single or discrete outcome is zero.
Distributed Cognition in Teams Is Influenced by Type of Task and Nature of Member Interactions
Published in Michael D. McNeese, Eduardo Salas, Mica R. Endsley, Foundations and Theoretical Perspectives of Distributed Team Cognition, 2020
R. Scott Tindale, Jeremy R. Winget, Verlin B. Hinsz
A more recent technique is the use of prediction markets (cf. Wolfers & Zitzewitz, 2004). Much like financial markets, prediction markets use buyers’ willingness to invest in alternative events (e.g., Great Britain will vote to stay vs. leave the European Union, the U.S. will launch a cyber-attack against Iran in the next year, etc.) as a gauge of their likelihood. They typically do not prohibit direct communication among forecasters/investors/bettors, but in usual practice, there is little, if any, communication. However, because the value placed on the assets is typically set in an open market of buyers and sellers, those already in (or out) of the markets can be informed and swayed by various market indicators (e.g., movements in prices, trading volume, volatility), and thus mutual social influence can occur through such channels. Prediction markets are a dynamic and continuous aggregation process in which bids and offers can be made, accepted, and rejected by multiple parties, and the collective expectations of the “group” can continue to change right up to the occurrence of the event in question (e.g., an election). Except for those with ulterior motives (e.g., to manipulate the market, or to use the market as a form of insurance), investments in such markets are likely to reflect the investors’ honest judgments about the relative likelihood of events. Members can use current market values to adjust their thinking and learn from the behavior of other members. However, such investment choices are not accompanied by any explanation or justification. Indeed, such investors may even have incentives to withhold vital information that would make other investors’ choices more accurate (e.g., that might inflate the price of a “stock” one wants to accumulate). Thus, in terms of opportunities for mutual education and persuasion, prediction markets fall somewhere between statistical aggregation methods (which allow none) and face-to-face groups (which allow many).
Personality information sharing in supply chain systems for innovative products in the circular economy era
Published in International Journal of Production Research, 2021
Chang Fang, Xiuyan Ma, Jin Zhang, Xide Zhu
Given the probability density function , a function is called the relative likelihood function if it satisfies that for all and .