Persistent and snap decision‐making

To avoid unfavorable inferences about her ability, an expert might cling to her original opinion and ignore valuable new information in formulating subsequent opinions. Conceivably, the expert might decline an initial opportunity to offer an opinion, delaying the opinion formation until more accurate information has arrived. However, we show that reputational concerns often lead an expert to express an opinion at the first opportunity, thereby making a snap decision.     

Opinion dynamics and wisdom under conformity

We study a dynamic model of opinion formation in social networks. In our model, boundedly rational agents update opinions by averaging over their neighbors’ expressed opinions, but may misrepresent their own opinion by conforming or counter-conforming with their neighbors. We show that an agent׳s social influence on the long-run group opinion is increasing in network centrality and decreasing in conformity. Concerning efficiency of information aggregation or “wisdom” of the society, it turns out that misrepresentation of opinions need not undermine wisdom, but may even enhance it. Given the network, we provide the optimal distribution of conformity levels in the society and show which agents should be more conforming in order to increase wisdom.     

Vergleich zweier Testverfahren zur Symmetrieprüfung in 4-Felder-Tafeln

In order to jugde the success of an election campaign, two opinion surveys are to be carried out at two different times, each timen people being asked about their opinion with respect to the partyx. Now the question arises whether we should ask the same sample ofn people both times (test of McNemar for the comparison of dependent frequencies) or whether it would be more suitable to carry out the second survey independently of the first one (test for the comparison of independent frequencies). In the present paper we calculate the asymptotic power functions of these two test procedures and derive the asymptotic relative efficiency (ARE).     

Information flows and crashes in dynamic social networks

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

     

Fakers becoming believers: how opinion dynamics are shaped by preference falsification,impression management and coherence heuristics

In the vast and rich literature on opinion dynamics, the role of preference falsification has generally been dismissed. Following the lead of Timur Kuran, in this paper we present one of the first multi-agent models that explores how opinion dynamics can be affected by the possible divorce between private and public opinions. It is also the first attempt to explore the role of social hierarchies in opinion dynamics conditioned by preference falsification. Our model formalizes heterogeneous evolving agents guided by a cognitively feasible set of heuristics and embedded in a social-rank-dependent structure of interactions. In social-rank-heterophilic encounters where people experience a high pressure of face-to-face interactions, unanimous support for the high social-rank preferred option emerges, while in any other scenario this option gathers majority but not unanimous support. Preference falsification has a crucial role in the emergence of unanimity, but it also creates the conditions for further private opinion actualizations that end up generating a self-sustained and sincere unanimity. When social-rank-homophilic encounters are the rule, or when group dynamics are irrelevant for opinion expression, agents never find incentives to falsify their opinions, therefore generating a social situation that resembles the general idea behind the ethnographic work of James C. Scott: true opinion expression in daily social-rank-homophilic encounters and a persistent opinion falsification in dissimilar social-rank interactions.     

Social networks,mass media and public opinions

Our opinions and ideas are shaped by what our friends said and what we read or watched on mass media. In this paper, we propose a concise and analyzable model to study the effects of mass media modeled as an applied external field, and social networks on public opinions based on the multi-state voter model, and a tuned parameter can control the relative intensity of the effects of mass media and social networks. We consider a generalized scenario where there exist committed or stubborn agents in the networks whose opinions are not affected by their friends or mass media. We find that the fraction of each opinion will converge to a value which only relates to the fractions and degrees of stubborn agents, and the relative intensity between media and network effects. The final agents with media opinion, except the stubborn agents, also include the increment produced by the internal impact of social networks and that caused by the external impact of media. Interestingly the second increment is composed of two parts, one is from the media effect when there are no interactions between agents and the other is from the influence of media on agent opinions caused by social network structure. That is the interactions among agents within social networks can amplify media influence. Finally we also discuss several extensions to the dynamics model which consider more realistic scenarios.     

On the monitoring of multi-attributes high-quality production processes

Over the last decade, there have been an increasing interest in the techniques of process monitoring of high-quality processes. Based upon the cumulative counts of conforming (CCC) items, Geometric distribution is particularly useful in these cases. Nonetheless, in some processes the number of one or more types of defects on a nonconforming observation is also of great importance and must be monitored simultaneously. However, there usually exist some correlations between these two measures, which obligate the use of multi-attribute process monitoring. In the literature, by assuming independence between the two measures and for the cases in which there is only one type of defect in nonconforming items, the generalized Poisson distribution is proposed to model such a problem and the simultaneous use of two separate control charts (CCC & C chats) is recommended. In this paper, we propose a new methodology to monitor multi-attribute high-quality processes in which not only there exist more than one type of defects on the observed nonconforming item but also there is a dependence structure between the two measures. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewnesses. Then, we estimate the transformed mean vector and covariance matrix and apply the well-known χ² control chart. In order to illustrate the proposed method and evaluate its performance, we use two numerical examples by simulation and compare the results. The results of the simulation studies are encouraging.     

The dual attitudinal dynamics of public opinion: an agent-based reformulation of L. F. Richardson’s war-moods model

This study proposes a new model of public opinion dynamics, focused specifically on attitudes toward war. The model consists of citizen-agents who have dual (public and private) attitudes. While they change the two levels of attitude according to different rules of two-layered interactions with neighboring agents, they also change their own attitudes internally. Our model shows that public opinion has “meta-stable” states, which generate overlapping, or two-valued, stable states. This characteristic can explain how public opinion responds to the outbreak and continuation of war very differently, not just in our model, but also in the opinions of European citizens on the First World War and those of American citizens on U.S. wars after the Second World War.     

Decisioni di gruppo e consenso: Un modello nell'ambito della teoria degli insiemi sfocati

Si propone un modello di consenso in ambiente sfumato, simile al cosiddetto metodo Delphi.Nel nostro approccio opinioni individuali e pesi sono numeri sfumati e la transizione tra stati è ottenuta per mezzo di operazioni max-min estese.Nel nostro approccio si prova che, dato un gruppo din individui, sotto opportune condizioni dopon–1 iterazioni le opinioni individuali si stabilizzano.

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Here we propose a model of consensus in a fuzzy environment similar to the so called Delphi method.In our approach individual opinions and weights are fuzzy numbers and transition from one state to another one is obtained via extended max-min operations.While in the classical stochastic approach to Delphi method (see De Groot [1] and Kelly [6]) conditions for reaching consensus are established, in our approach it is proved that, given a group ofn individuals, under suitable conditions aftern–1 iterations individual opinions become stable.

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Lavoro eseguito con il contributo CNR n. 83-02619.10.     

Discretionary Accruals and Auditor Behaviour in Code-Law Contexts: An Application to Failing Spanish Firms

This study examines the relationship between earnings management and auditor behaviour in the pre-bankrupt client segment of the Spanish audit market. As proxies for auditor behaviour, we use type of audit firm (Big N/non-Big N) and type of audit report. In contrast to the USA, audit reports in Spain often include modifications other than a going-concern opinion. This allows us to study the relationship in more detail than is possible with US data. The results of our study show that discretionary accruals are negatively related to going-concern opinions but are positively related to reports modified for reasons other than going-concern problems. However, unlike Butler et al. (Journal of Accounting and Economics, 37, pp. 139–165, 2004) the negative relationship is explained not by liquidity survival tactics but by auditor conservatism. We find this conservatism not only in the value of discretionary accruals but also in the qualifications that accompany a going concern. In these cases GAAP violations have a much greater income effect and a stronger relationship with the reversal of manipulation accumulated over the years than with the manipulation introduced during the last year. Finally, our results suggest that Big N differentiation in a code-law country is context-specific and depends on the business risk parameter of the ‘audit risk model’. In particular, for high-risk firms, Big N auditors show a significantly lower level of discretionary accruals and a greater propensity to issue a going-concern opinion.     

Limit theorems for reflected Ornstein–Uhlenbeck processes

This paper studies one‐dimensional Ornstein–Uhlenbeck (OU) processes, with the distinguishing feature that they are reflected on a single boundary (put at level 0) or two boundaries (put at levels 0 and d > 0). In the literature, they are referred to as reflected OU (ROU) and doubly reflected OU (DROU), respectively. For both cases, we explicitly determine the decay rates of the (transient) probability to reach a given extreme level. The methodology relies on sample‐path large deviations, so that we also identify the associated most likely paths. For DROU, we also consider the ‘idleness process’ L_t and the ‘loss process’ U_t, which are the minimal non‐decreasing processes, which make the OU process remain ≥ 0 and ≤ d, respectively. We derive central limit theorems (CLTs) for U_t and L_t, using techniques from stochastic integration and the martingale CLT.     

Estimation and testing of crossing‐points in fixed design regression

Many phenomena in the life sciences can be analyzed by using a fixed design regression model with a regression function m that exhibits a crossing‐point in the following sense: the regression function runs below or above its mean level, respectively, according as the input variable lies to the left or to the right of that crossing‐point, or vice versa. We propose a non‐parametric estimator and show weak and strong consistency as long as the crossing‐point is unique. It is defined as maximizing point arg max of a certain marked empirical process. For testing the hypothesis H₀ that the regression function m actually is constant (no crossing‐point), a decision rule is designed for the specific alternative H₁ that m possesses a crossing‐point. The pertaining test‐statistic is the ratio max/argmax of the maximum value and the maximizing point of the marked empirical process. Under the hypothesis the ratio converges in distribution to the corresponding ratio of a reflected Brownian bridge, for which we derive the distribution function. The test is consistent on the whole alternative and superior to the corresponding Kolmogorov–Smirnov test, which is based only on the maximal value max. Some practical examples of possible applications are given where a certain study about dental phobia is discussed in more detail.     

The Naming Game in social networks: community formation and consensus engineering

We study the dynamics of the Naming Game (Baronchelli et al. in J Stat Mech Theory Exp P06014, 2006b) in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.     

Audit Fees in a Joint Audit Setting

Abstract

This paper uses the uniqueness of the French audit environment to conduct an in-depth study of audit pricing issues associated with the requirement to hire two independent auditors (joint audit). I use a model derived from Simunic's [(1980). The pricing of audit services: Theory and evidence. Journal of Accounting Research, 18(1), 161–190] seminal work to examine to what extent audit fees are influenced by the number of Big 4 joint auditors (zero, one, or two). After controlling for well-known drivers of audit fees that are specific to audit client firms (size, complexity, and risk), for governance characteristics and for auditor selection, the paper shows that the decision to hire two Big 4 auditors as joint auditors does not require the payment of a higher Big 4 premium compared to the choice of one Big 4 auditor paired with a smaller auditor, other things being equal. The choice of two Big 4 auditors thus appears to be a rational economic choice for large and international firms.     

Measuring opinion distributions

In this study it will be argued that the perceived distribution of opinions among others is important for opinion research. Three different ways of measuring the perception of opinion distributions in survey research are compared: (a) by means of a questionwhat most people think about an issue, (b) by means of a questionhow many people are perceived to agree with an issue-statement, (c) by means of ‘line-production-boxes’, a special version ofmagnitude estimation. The results indicate that ‘line-production-boxes’ can improve data quality, but have also some drawbacks which will have to be dealt with. ‘Line-production-boxes’ give a wealth of information about individual differences in the forms of perceived opinion distributions. Although the normal distribution is used often, many other distribution forms are also used. The method of ‘line-production-boxes’ is compared with the method of estimating percentage points. Although high correlations suggest a good concurrent validity, some systematic differences do exist. New research directions are suggested.     

Unawareness and indifference to economic reform among the public: evidence from India’s power sector reform

Although economic reform generates winners and losers, many people have no opinion whatsoever about it. Because most empirical research ignores these non-responses, the conventional wisdom on the determinants of support for economic reform ignores large groups of silent citizens. To correct this problem, we present a stylized model that accounts for support, opposition, indifference, and unawareness about reform. We argue that informed people and those who perceive the status quo as dysfunctional will form an opinion more readily than others. For evidence, we examine public opinion about electricity privatization from a large field survey in rural India. We find that information and perceived inefficiency have much larger effects on the likelihood of forming an opinion than on the direction of that opinion (yes or no), emphasizing the importance of accounting for opinion formation process. In this case, information and perceived inefficiency make reform a salient issue to a passive public, most of whom become vocal opponents of reform.     

Dynamics of clustered opinions in complex networks

A simple model for simulating tug of war game as varying the player number in a team is discussed to identify the slow pace of fast change. This model shows that a large number of information sources leads slow change for the system. Also, we introduce an opinion diffusion model including the effect of a high degree of clustering. This model shows that the de facto standard and lock-in effect, well-known phenomena in economics and business management, can be explained by the network clusters.     

Expert opinion versus expertise in forecasting

Expert opinion is an opinion given by an expert, and it can have significant value in forecasting key policy variables in economics and finance. Expert forecasts can either be expert opinions, or forecasts based on an econometric model. An expert forecast that is based on an econometric model is replicable, and can be defined as a replicable expert forecast (REF), whereas an expert opinion that is not based on an econometric model can be defined as a non-replicable expert forecast (Non-REF). Both REF and Non-REF may be made available by an expert regarding a policy variable of interest. In this paper, we develop a model to generate REF, and compare REF with Non-REF. A method is presented to compare REF and Non-REF using efficient estimation methods, and a direct test of expertise on expert opinion is given. The latter serves the purpose of investigating whether expert adjustment improves the model-based forecasts. Illustrations for forecasting pharmaceutical stock keeping unit (SKUs), where the econometric model is of (variations of) the autoregressive integrated moving average model (ARIMA) type, show the relevance of the new methodology proposed in the paper. In particular, experts possess significant expertise, and expert forecasts are significant in explaining actual sales.     

Is gravity linear?

Despite the solid theoretical foundation on which the gravity model of bilateral trade is based, empirical implementation requires several assumptions which do not follow directly from the underlying theory. First, unobserved trade costs are assumed to be a (log‐)linear function of observables. Second, the effects of trade costs on trade flows are assumed to be constant across country pairs. Maintaining consistency with the underlying theory, but relaxing these assumptions, we estimate gravity models—in levels and logs—using two data sets via nonparametric methods. The results are striking. Despite the added flexibility of the nonparametric models, parametric models based on these assumptions offer equally or more reliable in‐sample predictions and out‐of‐sample forecasts in the majority of cases, particularly in the levels model. Moreover, formal statistical tests fail to reject either parametric functional form. Thus, concerns in the gravity literature over functional form appear unwarranted, and estimation of the gravity model in levels is recommended. Copyright © 2008 John Wiley & Sons, Ltd.     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.