LEARNING FROM THE EXPECTATIONS OF OTHERS

This paper explores the equilibrium properties of boundedly rational heterogeneous agents under adaptive learning. In a modified cobweb model with a Stackelberg framework, there is an asymmetric information diffusion process from leading to following firms. It turns out that the conditions for at least one learnable equilibrium are similar to those under homogeneous expectations. However, the introduction of information diffusion leads to the possibility of multiple equilibria and can expand the parameter space of potential learnable equilibria. In addition, the inability to correctly interpret expectations will cause a “boomerang effect” on the forecasts and forecast efficiency of the leading firms. The leading firms' mean square forecast error can be larger than that of following firms if the proportion of following firms is sufficiently large.

Download Full-text

Simulating the Information Diffusion Process in Complex Networks Using Push and Pull Strategies

2014 European Network Intelligence Conference ◽

10.1109/enic.2014.22 ◽

2014 ◽

Cited By ~ 3

Author(s):

Dariusz Krol ◽

Marcin Budka ◽

Katarzyna Musial

Keyword(s):

Diffusion Process ◽

Complex Networks ◽

Information Diffusion ◽

Push And Pull

Download Full-text

Non-Markovian effect of the fractional damping environment and Newton’s second law of motion

Modern Physics Letters B ◽

10.1142/s021798491850210x ◽

2018 ◽

Vol 32 (19) ◽

pp. 1850210

Author(s):

Chun-Yang Wang ◽

Zhao-Peng Sun ◽

Ming Qin ◽

Yu-Qing Xu ◽

Shu-Qin Lv ◽

...

Keyword(s):

Diffusion Process ◽

Mean Square Displacement ◽

Dynamical Analysis ◽

Second Law ◽

Mean Square ◽

Fractional Damping ◽

Dynamical Mechanism ◽

Brownian Particles ◽

The Mean ◽

Dissipative Environments

We report, in this paper, a recent study on the dynamical mechanism of Brownian particles diffusing in the fractional damping environment, where several important quantities such as the mean square displacement (MSD) and mean square velocity are calculated for dynamical analysis. A particular type of backward motion is found in the diffusion process. The reason of it is analyzed intrinsically by comparing with the diffusion in various dissipative environments. Results show that the diffusion in the fractional damping environment obeys the Langevin dynamics which is quite different form what is expected.

Download Full-text

An immigration super-critical branching diffusion process

Journal of Applied Probability ◽

10.1017/s002190020009464x ◽

1972 ◽

Vol 9 (01) ◽

pp. 13-23 ◽

Cited By ~ 1

Author(s):

J. Radcliffe

Keyword(s):

Diffusion Process ◽

Random Variable ◽

Mean Square ◽

Branching Diffusion ◽

Mean Square Convergence

This paper is an extension of Davis (1965) by allowing immigration. Mean square convergence is proved for a random variable in a branching diffusion process allowing immigration.

Download Full-text

A Multidude of Econometric Tests: Forecasting the Dutsch Guilder

International Journal of Economics and Finance ◽

10.5539/ijef.v9n9p94 ◽

2017 ◽

Vol 9 (9) ◽

pp. 94

Author(s):

Augustine C. Arize ◽

Ioannis N. Kallianiotis ◽

Ebere Eme Kalu ◽

John Malindretos ◽

Moschos Scoullis

Keyword(s):

Exchange Rate ◽

Error Correction ◽

Forecast Error ◽

Predictive Performance ◽

Mean Square ◽

Rate Model ◽

Long Run ◽

Exchange Rate Models ◽

Econometric Tests ◽

Nonparametric Techniques

This paper studies a diversity of exchange rate models, applies both parametric and nonparametric techniques to them, and examines said models’ collective predictive performance. We shall choose the forecasting predictor with the smallest root mean square forecast error (RMSE); the empirical evidence for a better type of exchange rate model is in equation (34), although none of our evidence gives an optimal forecast. At the end, these models’ error correction versions will be fit so that plausible long-run elasticities can be imposed on each model’s fundamental variables.

Download Full-text

Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

Vietnam Journal of Computer Science ◽

10.1142/s2196888821500032 ◽

2020 ◽

Vol 08 (01) ◽

pp. 93-112

Author(s):

Péter Marjai ◽

Attila Kiss

Keyword(s):

Diffusion Process ◽

Complex Networks ◽

Relative Entropy ◽

Information Diffusion ◽

Dynamic Networks ◽

Centrality Measures ◽

Topsis Method ◽

Cover Time ◽

Influential Nodes ◽

Different Characteristics

For decades, centrality has been one of the most studied concepts in the case of complex networks. It addresses the problem of identification of the most influential nodes in the network. Despite the large number of the proposed methods for measuring centrality, each method takes different characteristics of the networks into account while identifying the “vital” nodes, and for the same reason, each has its advantages and drawbacks. To resolve this problem, the TOPSIS method combined with relative entropy can be used. Several of the already existing centrality measures have been developed to be effective in the case of static networks, however, there is an ever-increasing interest to determine crucial nodes in dynamic networks. In this paper, we are investigating the performance of a new method that identifies influential nodes based on relative entropy, in the case of dynamic networks. To classify the effectiveness, the Suspected-Infected model is used as an information diffusion process. We are investigating the average infection capacity of ranked nodes, the Time-Constrained Coverage as well as the Cover Time.

Download Full-text

An epidemic model for correlated information diffusion in crowd intelligence networks

International Journal of Crowd Science ◽

10.1108/ijcs-01-2019-0005 ◽

2019 ◽

Vol 3 (2) ◽

pp. 168-183 ◽

Cited By ~ 1

Author(s):

Yuejiang Li ◽

H. Vicky Zhao ◽

Yan Chen

Keyword(s):

Diffusion Process ◽

Mixture Model ◽

Information Diffusion ◽

Stable State ◽

Stable Condition ◽

Real Data ◽

Information Flows ◽

Content Type ◽

Correlated Information ◽

Crowd Intelligence

Purpose With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks. Design/methodology/approach This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them. Findings The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model. Originality/value The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

Download Full-text

Taking Sides in Wars of Attrition

American Political Science Review ◽

10.1017/s0003055416000782 ◽

2017 ◽

Vol 111 (2) ◽

pp. 219-236 ◽

Cited By ~ 7

Author(s):

ROBERT POWELL

Keyword(s):

Asymmetric Information ◽

Continuous Time ◽

Formal Analysis ◽

Third Parties ◽

The Other ◽

Third Party ◽

War Of Attrition ◽

The Third ◽

Boomerang Effect ◽

Formal Framework

Third parties often have a stake in the outcome of a conflict and can affect that outcome by taking sides. This article studies the factors that affect a third party's decision to take sides in a civil or interstate war by adding a third actor to a standard continuous-time war of attrition with two-sided asymmetric information. The third actor has preferences over which of the other two actors wins and for being on the winning side conditional on having taken sides. The third party also gets a flow payoff during the fighting which can be positive when fighting is profitable or negative when fighting is costly. The article makes four main contributions: First, it provides a formal framework for analyzing the effects of endogenous intervention on the duration and outcome of the conflict. Second, it identifies a “boomerang” effect that tends to make alignment decisions unpredictable and coalitions dynamically unstable. Third, it yields several clear comparative-static results. Finally, the formal analysis has implications for empirical efforts to estimate the effects of intervention, showing that there may be significant selection and identification issues.

Download Full-text

Trend Models for Analysis of Socio-Economic Security

Science & Technique ◽

10.21122/2227-1031-2020-19-2-108-112 ◽

2020 ◽

Vol 19 (2) ◽

pp. 108-112

Author(s):

D. N. Shvaiba

Keyword(s):

Time Series ◽

Forecast Error ◽

Economic Security ◽

Practical Significance ◽

Mean Square ◽

Proposed Model ◽

Upper Limits ◽

Selection For ◽

Random Nature ◽

Selection Of

Correctness of the trend selection for predicting characteristics of socio-economic security statistics can be qualified with the help of a mean square error value and an aspect of “Ascending” and “Descending” series (although there are other aspects, for example, the aspects based on the median of a sample). According to the proposed model, it is possible to predetermine average monitoring errors for development of lower and upper limits of the forecast version in respect of values for characteristics of socio-economic security statistics. Model creation is a labor-intensive process, so that when predicting characteristics of socio-economic security statistics, it is advisable to use, as a rule, a deterministic component of trend models. At the same time, an assumption about random nature of deviations in empirical values of time series from a trend for 5 % significance value is not rejected. Study of the material allows us to admit that it is impossible to note exact cycles in time series of values for characteristics of socio-economic security statistics. However, this does not represent a basis for the conclusion about presence of cycles in time series of values for characteristics of socio-economic security statistics because these cycles do not coincide in time, there is no clear priority in exceedance of actual values for characteristics of socio-economic security statistics over the calculated ones obtained with the help of models, or, on the contrary, exceedance of the calculated values over the actual ones. Various approaches can be used to calculate a magnitude of the forecast error. Thus, a question pertaining to selection of trend models for an analysis of socio-economic security is natural due to difference in reliability of data when using different models, and correctness of the selection will improve an efficiency of the analysis. So the study acquires practical significance for economic entities and entire industries

Download Full-text