1,720,995 research outputs found
False Consensus Effect and intertemporal choices in multi-agent decision problems
No full textPsychological evidences of impulsivity and false consensus effect lead results far from
rationality. It is shown that impulsivity modifies the discount function of each individual, and
false consensus effect increases the degree of consensus in a multi-agent decision problem.
Analyzing them together we note that in strategic interactions these two human factors
involve choices which change equilibriums expected by rational individua
A behavioural model of hyperbolic discounting in the framework of force of mortality
No full textThis paper has suggested an interpretation that when an agent is time consistent, diminishing impatience may be related to the subjective diminishing hazard rate of discontinuance of future consumption. Beyond casual observation and support from psychological theoretical and experimental studies that justify this relation, it has been demonstrated that diminishing time discounting may be supported by realistic demographic models. The latter allow for unobservable heterogeneity in frailty, about which the agent learns as time passes
Assessing false consensus effect in a consensus enhancing procedure
No full textA model for Multi Expert Multi Criteria Decision Making (ME-MCDM) is proposed and defined in a consensus reaching process. A decision problem is considered, in which a group of experts is involved in the evaluation of the performances of a set of alternatives with respect to a predefined set of criteria. The main novelty of the present consensus model is that of being guided by both consensus and false consensus effects.
One finding in studies examining intuitive predictions of the preference is the false consensus effect, that represents the tendency to overestimate consensus for one’s attitudes and behaviours. The purpose is to evaluate a consensual judgement whether the consensus degree is partly due to expert’s failure to recognize that their choices not only depend on the ‘objective’ response alternatives, but also on their subjective structure. In this context, expert’s own beliefs, values and habits tend to bias their perception of how widely they are shared. Also the consensus reaching process is guided automatically, without moderator. To do that, the entire process is modelled within fuzzy set theory by Ordered Weighted Averaging (OWA) operators. Our study contributes by investigating the magnitude of the agreement in the case of presence of the false consensus effect
Fraud Measurement using Ordered Weighted Aggregation
No full textThe expansion of modern technologies and global communication is giving rise to
a dramatic increase of frauds, resulting in the loss of billions of dollars world-wide each
year. Fraud is an ‘unpleasant and expensive reality that all banks, retailers and credit granting
companies face. It is important to provide expertise to help financial institutions to resolve
and recover assets and develop a solid business experience and base its policy on successful
fraud prevention and recovery. The aim of the present paper is to investigate the use of
ordered weighted averaging (OWA) operators and their extensions fur fraud measurement
in financial transactions.We consider the fraudulent behaviour in some components (criteria)
and argue that the aggregation of the corresponding information can be effectively carried
on introducing a parameterized family of aggregation operators (OWA,GOWA) that provide
a fusion of pieces of information when the selection of the weights supports the modelling of
some aggregation imperative depending on the rationality of the experts
Multi-Expert Consensus in Strategic Portfolio Evaluation using Discrete Choquet Integral for Analytical Network Process Weights
No full textCustomer profiling tools have been introduced in recent years to protect
investors against the increasing complexity of financial instruments. Advisor
experts have to combine measures of finance and behavioural finance studies
in their evaluation. The need to integrate different competences and compare
complex criteria naturally leads to the modelling of the portfolio selection
problem as a group decision problem. The Analytic Network Process (ANP) is a useful technique that helps decision-makers evaluate the available alternatives
and aggregate their professional knowledge. In this context, the Reaching
Consensus Process (RCP) is crucial for the validity of choices due to the strong
sensitivity of the ANP to the decision maker's judgement. However, the RCP
may be affected by the behavioural characteristics of experts, the vulnerability
of judgements and the uncertainty that characterises the decision-making
context, leading to unbiased judgements that are not based on different
expertise. Similarly, without the involvement of more experts, the process
could be inefficient due to the sensitivity of the ANP decision-maker’s
judgement. In order to maximise the aggregation of experts' knowledge, to
minimise the influence of the individual in quantifying the relationships
between criteria and alternatives, and to preserve the impartiality of
evaluations, the present paper discusses RCP through Choquet’s discrete
integral. This aggregation captures the non-linearity of preferences and
includes the fuzziness of judgments. Thus, there is no need to reach a
consensus when evaluating the criteria: each expert manages a single ANP
whose synthesis are aggregated using Choquet’s discrete integral. In this
sense, the aggregation defines an unbiased consensus which mitigates the
sensitivity of the ANP to the preferences of the decision-maker. The
methodology is applied to a case study in which several experts evaluate
investment alternatives of an individual, simultaneously incorporating
behavioural criteria and Markets in Financial Instruments Directive (MiFID)
client profiling criteria
Modeling false consensus, vol. 5 (no. 2), pp. 1 – 20. Alexander Myller Publishing, Iasi. ISSN: 2248 – 1672; ISSN-L: 2248-1672
No full textFrom E-learning to financial mathematics
No full textThe mobile computing is a daily standard: mobile phones, Personal Digital Assistants (PDA), smart vehicles, GPS, Smartphones, Tablet PCs, all technologies that are more or less connected with the real needings of the users, providing information to the user on the move. This fact suggests to teachers and students to experiment new interaction perspectives and new teaching/learning methods. Mobile learning is considered as a basis in the future of the different forms of ITC assisted learning methods, even if the existing experimentations in the field are very few. On the other hand, there are still many points to make clear, and not only from a strictly technical point of view. Among those, the most relevant are: • Which is the right learning model for the learning process when the communication is mediated by mobile devices? • How does a model involving m-learning techniques affect the students’ learning methods? • Which are the most useful tools to evaluate the performances of the teaching/learning process if this is ICT-based? • Which type of services can be set up, with profit both for teachers and students, considering both the actual limits and the great potentialities descending from those mobile systems? • What is the impact of this new teaching method on the traditional teaching method we are used to see since the origin of human societies? This paper presents an analysis of these issues, referring to experiences in progress regarding the projecting and the realization of e-learning platforms, equipped with extensions for mobile technologies, and it also refers to a project based on the purchase of the mobile technologies to create a learning community related to Financial Mathematical issues
Analyzing AHP matrix by robust regression
No full textThe Analytic Hierarchy Process (AHP) is a powerful process to help
people to express priorities and make the best decision when both qualitative and
quantitative aspects of a decision need to be considered. In this paper, in order to
eliminate the in°uence of outliers, we use an approach based on Robust Partial Least
Squares (R-PLS) regression for the computation of the values for the weights of
a comparison matrix. A simulation study to compare the results with other methods
for computing the weights proposed to analyze comparison matrix
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