Bayesian Updating Rules in Continuous Opinion Dynamics Models

In this article, I investigate the use of Bayesian updating rules applied to modeling social agents in the case of continuos opinions models. Given another agent statement about the continuous value of a variable $x$, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a Uniform distribution. This represents the idea the other agent might have no idea about what he is talking about. The effect of updating only the first moments of the distribution will be studied. and we will see that this generates results similar to those of the Bounded Confidence models. By also updating the second moment, several different opinions always survive in the long run. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.Comment: 14 pages, 5 figures, presented at SigmaPhi200

C-IPS: Specifying decision interdependencies in negotiations

Abstract. Negotiation is an important mechanism of coordination in multiagent systems. Contrary to early conceptualizations of negotiating agents, we believe that decisions regarding the negotiation issue and the negotiation partner are equally important as the selection of negotiation steps. Our C-IPS approach considers these three aspects as separate decision processes. It requires an explicit specification of interdependencies between them. In this article we address the task of specifying the dynamic interdependencies by means of IPS dynamics. Thereby we introduce a new level of modeling negotiating agents that is above negotiation mechanism and protocol design. IPS dynamics are presented using state charts. We define some generally required states, predicates and actions. We illustrate the dynamics by a simple example. The example is first specified for an idealized scenario and is then extended to a more realistic model that captures some features of open multiagent systems. The well-structured reasoning process for negotiating agents enables more comprehensive and hence more flexible architectures. The explicit modeling of all involved decisions and dependencies eases the understanding, evaluation, and comparison of different approaches to negotiating agents.

Mobility and Social Network Effects on Extremist Opinions

Understanding the emergence of extreme opinions and in what kind of environment they might become less extreme is a central theme in our modern globalized society. A model combining continuous opinions and observed discrete actions (CODA) capable of addressing the important issue of measuring how extreme opinions might be has been recently proposed. In this paper I show extreme opinions to arise in a ubiquitous manner in the CODA model for a multitude of social network structures. Depending on network details reducing extremism seems to be possible. However, a large number agents with extreme opinions is always observed. A significant decrease in the number of extremists can be observed by allowing agents to change their positions in the network.Comment: 7 pages, 8 figures, discussion expanded, new references, new figure

Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach

Introduction. The objects of investigation of this work are micro-level behaviors in stock markets. We aim at better understanding which strategies of market participants drive stock markets. The problem is that micro-level data from real stock markets are largely unobservable. We take an estimation perspective to obtain daily time series of fractions of chartists and fundamentalists among market participants. We estimate the heterogeneous agent-based financial market model introduced by Lux and Marchesi [1] to the S&P 500. This model has more realistic time series properties compared to less complex econometric and other agent-based models. Such kinds of models have a rather complex dependency between micro and macro parameters that have to be mapped to empirical data by the estimation method. This poses heavy computational burdens. Our contribution to this field is a new method for indirectly estimating time-varying micro-parameters of highly complex agent-based models at high frequency. Related work. Due to the high complexity, few authors have published on this topic to date (e.g., [2], [3], and [4]). Recent approaches in directly estimating agent-based models are restricted to simpler models, make simplifying assumptions on the estimation procedure, estimate only non-time varying parameters, or estimate only low frequency time series. Approach and computational methods. The indirect estimation method we propose is based on estimating the inverse model of a rich agent-based model that derives realistic macro market behavior from heterogeneous market participants’ behaviors. Applying the inverse model, which maps macro parameters back to micro parameters, to widely available macro-level financial market data, allows for estimating time series of aggregated real world micro-level strategy data at daily frequency. To estimate the inverse model in the first place, a neural network approach is used, as it allows for a large degree of freedom concerning the structure of the mapping to be represented by the neural network. As basis for learning the mapping, micro and macro time series of the market model are generated artificially using a multi-agent simulation based on RePast [5]. After applying several pre-processing and smoothing methods to these time series, a feed-forward multilayer perceptron is trained using a variant of the Levenberg-Marquardt algorithm combined with Bayesian regularization [6]. Finally, the trained network is applied to the S&P 500 to estimate daily time series of fractions of strategies used by market participants. Results. The main contribution of this work is a model-free indirect estimation approach. It allows estimating micro-parameter time series of the underlying agent-based model of high complexity at high frequency. No simplifying assumptions concerning the model or the estimation process have to be applied. Our results also contribute to the understanding of theoretical models. By investigating fundamental depen¬den¬cies in the Lux and Marchesi model by means of sensitivity analysis of the resulting neural network inverse model, price volatility is found to be a major driver. This provides additional support to findings in [1]. Some face validity for concrete estimation results obtained from the S&P 500 is shown by comparing to results of Boswijk et al. [3]. This is the work which comes closest to our approach, albeit their model is simpler and estimation frequency is yearly. We find support for Boswijk et al.’s key finding of a large fraction of chartists during the end of 1990s price bubble in technology stocks. Eventually, our work contributes to understanding what kind of micro-level behaviors drive stock markets. Analyzing correlations of our estimation results to historic market events, we find the fraction of chartists being large at times of crises, crashes, and bubbles. See also http://www.whodrivesthemarket.com for some continuously updated and derived live-results

Prosocial Behavior and Public Service Motivation

Although research on public service motivation (PSM) is vast, there is little evidence regarding the effects of PSM on observable behavior. This article contributes to our understanding of the behavioral implications of PSM by investigating whether PSM is associated with prosocial behavior. Moreover, we address if and how the behavior of other group members influences this relationship. The study uses the (pseudo-)experimental setting of the public goods game (the experimental part), run with a sample of 263 students, in combination with survey-based PSM measures (the non-experimental element). We find a positive link between PSM and prosocial behavior. Moreover, we reveal that this relationship is moderated by the behavior of other group members: High PSM people act even more prosocially when the other members of the group show prosocial behavior as well, but they do not do so if the behavior of other group members is not prosocial

Universality in movie rating distributions

In this paper histograms of user ratings for movies (1,...,10) are analysed. The evolving stabilised shapes of histograms follow the rule that all are either double- or triple-peaked. Moreover, at most one peak can be on the central bins 2,...,9 and the distribution in these bins looks smooth `Gaussian-like' while changes at the extremes (1 and 10) often look abrupt. It is shown that this is well approximated under the assumption that histograms are confined and discretised probability density functions of L\'evy skew alpha-stable distributions. These distributions are the only stable distributions which could emerge due to a generalized central limit theorem from averaging of various independent random avriables as which one can see the initial opinions of users. Averaging is also an appropriate assumption about the social process which underlies the process of continuous opinion formation. Surprisingly, not the normal distribution achieves the best fit over histograms obseved on the web, but distributions with fat tails which decay as power-laws with exponent -(1+alpha) (alpha=4/3). The scale and skewness parameters of the Levy skew alpha-stable distributions seem to depend on the deviation from an average movie (with mean about 7.6). The histogram of such an average movie has no skewness and is the most narrow one. If a movie deviates from average the distribution gets broader and skew. The skewness pronounces the deviation. This is used to construct a one parameter fit which gives some evidence of universality in processes of continuous opinion dynamics about taste.Comment: 8 pages, 5 figures, accepted for publicatio

Entrepreneurial Orientation: The Dimensions' Shared Effects on Explaining Firm Performance

We shed new light on the structure of the relationship between entrepreneurial orientation (EO) and firm performance and how this relationship varies across contexts. Using commonality analysis, we decompose the variance in performance—in terms of the effects of innovativeness, proactiveness, and risk taking—into parts that are attributable to unique variations in these dimensions (unique effects) and those attributable to covariation between these dimensions (shared effects). By demonstrating the empirical relevance of unique, bilaterally shared, and commonly shared effects in a heterogeneous sample of low–tech, high–tech, and multi–sector firms, we consolidate existing conceptualizations of EO and propose an extension of the extant theoretical views of the construct. </jats:p

Language in international business: a review and agenda for future research

A fast growing number of studies demonstrates that language diversity influences almost all management decisions in modern multinational corporations. Whereas no doubt remains about the practical importance of language, the empirical investigation and theoretical conceptualization of its complex and multifaceted effects still presents a substantial challenge. To summarize and evaluate the current state of the literature in a coherent picture informing future research, we systematically review 264 articles on language in international business. We scrutinize the geographic distributions of data, evaluate the field’s achievements to date in terms of theories and methodologies, and summarize core findings by individual, group, firm, and country levels of analysis. For each of these dimensions, we then put forward a future research agenda. We encourage scholars to transcend disciplinary boundaries and to draw on, integrate, and test a variety of theories from disciplines such as psychology, linguistics, and neuroscience to gain a more profound understanding of language in international business. We advocate more multi-level studies and cross-national research collaborations and suggest greater attention to potential new data sources and means of analysis

Economics education and value change: The role of program-normative homogeneity and peer influence

In the light of corporate scandals and the recent financial crisis, there has been an increased interest in the impact of business education on the value orientations of graduates. Yet our understanding of how students' values change during their time at business school is limited. In this study,weinvestigate the effects of variations in the normative orientations of economics programs. We argue that interaction among economics students constitutes a key mechanism of value socialization, the effects of which are likely to vary across more-or-less normatively homogeneous economics programs. In normatively homogeneous programs, students are particularly likely to adopt economics values as a result of peer interaction. We specifically explore changes in power, hedonism, and self-direction values in a 2-year longitudinal study of economics students (N 5 197) in a normatively homogeneous and two normatively heterogeneous economics programs. As expected, for students in a normatively homogeneous economics program, interaction with peers was linked with an increase in power and hedonism values, and a decrease in self-direction values. Our findings highlight the interplay between program normative homogeneity and peer interaction as an important factor in value socialization during economics education and have important practical implications for business school leaders