30,820 research outputs found
Persistent Disagreement and Polarization in a Bayesian Setting
For two ideally rational agents, does learning a finite amount of shared evidence necessitate agreement? No. But does it at least guard against belief polarization, the case in which their opinions get further apart? No. OK, but are rational agents guaranteed to avoid polarization if they have access to an infinite, increasing stream of shared evidence? No
The Effect of Communicating Ambiguous Risk Information on Choice
Decision makers are frequently confronted with ambiguous risk information about activities with potential hazards. This may be a result of conflicting risk estimates from multiple sources or ambiguous risk information from a single source. The paper considers processing ambiguous risk information and its effect on the behavior of a decision maker with a-maximin expected utility preferences. The effect of imprecise risk information on behavior is related to the content of information, the decision maker’s trust in different sources of information, and his or her aversion to ambiguity.a-Maximin Expected Utility, aggregation of expert opinions, ambiguity, Knightian uncertainty, risk communication, trust in information source, Risk and Uncertainty,
A Bayesian Framework for Combining Valuation Estimates
Obtaining more accurate equity value estimates is the starting point for
stock selection, value-based indexing in a noisy market, and beating benchmark
indices through tactical style rotation. Unfortunately, discounted cash flow,
method of comparables, and fundamental analysis typically yield discrepant
valuation estimates. Moreover, the valuation estimates typically disagree with
market price. Can one form a superior valuation estimate by averaging over the
individual estimates, including market price? This article suggests a Bayesian
framework for combining two or more estimates into a superior valuation
estimate. The framework justifies the common practice of averaging over several
estimates to arrive at a final point estimate.Comment: Citations at
http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=240309 Review of
Quantitative Finance and Accounting, 30.3 (2008) forthcomin
Regret Averse Opinion Aggregation
It is often suggested that when opinions differ among individuals in a group, the opinions should be aggregated to form a compromise. This paper compares two approaches to aggregating opinions, linear pooling and what I call opinion agglomeration. In evaluating both strategies, I propose a pragmatic criterion, No Regrets, entailing that an aggregation strategy should prevent groups from buying and selling bets on events at prices regretted by their members. I show that only opinion agglomeration is able to satisfy the demand. I then proceed to give normative and empirical arguments in support of the pragmatic criterion for opinion aggregation, and that ultimately favor opinion agglomeration
Regret Averse Opinion Aggregation
It is often suggested that when opinions differ among individuals in a group, the opinions should be aggregated to form a compromise. This paper compares two approaches to aggregating opinions, linear pooling and what I call opinion agglomeration. In evaluating both strategies, I propose a pragmatic criterion, No Regrets, entailing that an aggregation strategy should prevent groups from buying and selling bets on events at prices regretted by their members. I show that only opinion agglomeration is able to satisfy the demand. I then proceed to give normative and empirical arguments in support of the pragmatic criterion for opinion aggregation, and that ultimately favor opinion agglomeration
Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies
This work aims at investigating and quantifying the Urban Transport System
(UTS) resilience enhancement enabled by the adoption of emerging technology
such as Internet of Everything (IoE) and the new trend of the Connected
Community (CC). A conceptual extension of Functional Resonance Analysis Method
(FRAM) and its formalization have been proposed and used to model UTS
complexity. The scope is to identify the system functions and their
interdependencies with a particular focus on those that have a relation and
impact on people and communities. Network analysis techniques have been applied
to the FRAM model to identify and estimate the most critical community-related
functions. The notion of Variability Rate (VR) has been defined as the amount
of output variability generated by an upstream function that can be
tolerated/absorbed by a downstream function, without significantly increasing
of its subsequent output variability. A fuzzy based quantification of the VR on
expert judgment has been developed when quantitative data are not available.
Our approach has been applied to a critical scenario (water bomb/flash
flooding) considering two cases: when UTS has CC and IoE implemented or not.
The results show a remarkable VR enhancement if CC and IoE are deploye
- …