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.

     

A dynamic analysis of educational progression of children of immigrants

This paper uses a parsimonious version of Cameron and Heckman's (2001) dynamic model of educational progression to determine at what stages of their educational careers children of immigrants fall behind their native peers and the magnitude of intergenerational transmission. The analyses are primarily directed towards explaining high dropout rates from vocational upper secondary education, identified as the main barrier to educational attainment. The results corroborate previous findings that family background is an important determinant of educational outcomes but also suggest that differences in endowments alone do not explain the observed gap in educational attainment between natives and ethnic minorities.     

A dynamic analysis of informal care and employment in England

This paper analyzes the dynamics in employment and informal care outcomes of women in England. To this end, we develop a dynamic model to describe pathways leading to a negative correlation between informal care and employment in a cross-section. The model allows for different types of caregiving, correlated permanent unobserved heterogeneity and initial sorting. The model is estimated on data from 6 waves of the BHPS 2000–2005. Our findings suggest modest feedback effects. We find a negative effect of co-residential caregiving on future employment and a negative effect of employment on future co-residential and extra-residential caregiving. We also find evidence of positive state-dependence in caregiving although most of the persistence in such activities is related to unobserved heterogeneity rather than state-dependence.     

Structural investigation of supply networks: A social network analysis approach

A system of interconnected buyers and suppliers is better modeled as a network than as a linear chain. In this paper we demonstrate how to use social network analysis to investigate the structural characteristics of supply networks. Our theoretical framework relates key social network analysis metrics to supply network constructs. We apply this framework to the three automotive supply networks reported in Choi and Hong (2002). Each of the supply networks is analyzed in terms of both materials flow and contractual relationships. We compare the social network analysis results with the case-based interpretations in Choi and Hong (2002) and conclude that our framework can both supplement and complement case-based analysis of supply networks.     

交通机动化爆炸性增长的模型分析

近年来我国城市交通出现较为严重的拥挤,其中一个重要的背景原因就是机动化进程不但呈现加速趋势,而且其增长具有爆炸性,而以往对机动化水平的预测明显偏低。基于交通规划预测中常用的逻辑斯蒂(Logistic)模型分析了爆炸性机动化的形成机制,并在建立收入增长的动态模型的基础上进行了数值模拟,进而提出了相关的政策建议。     

Statistical analysis of change in networks

A survey is given of random graphs and random graph processes which can be used to describe and analyze networks that are changing with time. Marko-vian change over time, log-linear models for change, and conditionally uniform models for change are described. It is noted that estimation is usually complex if the random graph involves dependent dyads. Models with deterministic change over time may be a way to avoid the difficulties implied by dependent dyads. Logit regression methods are described that can be used to estimate such models.     

Coloured semantic networks for content analysis

This paper adapts a widespread formalism of Knowledge Representation known in the AI literature as J. Sowa'sConceptual Graphs to the purposes of Content Analysis. It is proposed that instead of nested contexts, negation and modalities could be represented by colouring the links and the nodes of the graphs. This colour-based representation of logic enables the replacement of Sowa's game-theoretical semantics by a matching algorithm. It is also claimed that the resulting local application of negation and modalities is close to the behaviour of negation and adverbs in natural language. Above all, the use of colours restores a unique level in semantic networks. These flat semantic networks are appealing to social scientists because they avoid information redundancy and improve connectivity in the network. Two methods of textual analysis that can take direct benefit from semantic networks (semantic paths distributions and texts clustering) are presented. However, flat semantic networks that remain semantically correct are perhaps even more interesting because they may bridge standard networks with connectionnist networks. In such a perspective, Coloured Conceptual Graphs represent an intermediate but promising step.     

Issues in the analysis of co-authorship networks

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.     

Design limits and dynamic policy analysis

This paper characterizes the frequency domain properties of feedback control rules in linear systems in order to better understand how different policies affect outcomes frequency by frequency. We are especially concerned in understanding how reductions of variance at some frequencies induce increases in variance at others. Tradeoffs of this type are known in the control literature as design limits. Design limits are important in understanding the full range of effects of macroeconomic stabilization policies. We extend existing results to account for discrete time bivariate systems with rational expectations. Application is made to the evaluation of monetary policy rules.     

The discrete sequential analysis of dynamic international behavior

This study reexamines the dynamic patterns of US-Soviet foreign policy behavior using recently developed methods for the sequential analysis of categorical data. The methods proposed here have several advantages over conventional regression based modeling procedures. First, assumptions about the role of time in foreign policy behavior and decisionmaking processes are more realistic in that time is represented as an ordinal sequence on human experience. Second, these methods better approximate the causal structure of actionreaction theories by delineating the temporal order of stimulus and response, and by focusing attention on individual behaviors rather than aggregate behavioral constructs. Third, the probabilistic nature of action-reaction hypotheses are made explicit through the use of logit transformed conditional probabilities. Finally, by accommodating model specifications that are virtually isomorphic to traditional action-reaction modeling efforts, these methods retain the flexibility and interpretability of standard regression approaches. Application of these methods to US-Soviet interaction revealed a marked asymmetry in the superpowers' responsiveness to one another's foreign policy behavior during the 1966–1978 period. According to these analyses, Soviet behavior is more clearly a function of its own prior activities than a response to the United States whereas American behavior is far more dependent on prior Soviet actions than its on own.     

企业动态能力对组织内部创新机制作用理论阐释

如何才能使我国企业更好地建立内部创业环境、制定内部创业规则以及激活内部创业活动,是在当前建设创新型社会的大背景下,对企业长远发展意义重大的问题之一。本丈试从当前理论界比较前沿的企业动态能力的角度出发,理论探讨这一命题,以期得出一些可以借鉴的结论。     

Knowledge transformation in project networks: A speech act level cross‐boundary analysis

Researchers stress the importance of understanding knowledge transformation in projects. To explore how knowledge is transformed across organizational and specialization boundaries in project networks, we observed 22 knowledge transformation cases in two project networks. We found that new knowledge was created across specialization boundaries and that knowledge was altered across organizational boundaries. When both organizational and specialization boundaries were crossed, new knowledge was created. From these findings, we developed a set of propositions and formulated these into a model of knowledge transformation in project networks. This research contributes to a better understanding of knowledge transformation processes and outcomes in project networks.     

对比解题——辩证分析动静法

动静法产生和发展于人们对静力学较为了解,但对动力学知之甚少的年代,其精髓在于通过引入虚加惯性力将动力学转化为静力学问题来解决,相对于后期发展起来的动力学知识而言,它有着自身的一些特点和优势,但同时也暴露出一些缺点,本文以动静法与近代动力学方法的对比为线索,通过两种方法的对比解题,就动静法的优势与缺点进行辩证分析与研究,旨在给动静法一个客观公正的评价。     

Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

A functional perspective on financial networks

The financial sector is a critical component of any economic system, as it delivers key qualitative asset transformation services in terms of liquidity, maturity and volume. Although these functions could in principle be carried out separately by specialized actors, in the end it is their systemic co-evolution that determines how the aggregate economy performs and withstands disruptions. In this paper we argue that a functional perspective on financial intermediation can be usefully employed to investigate the functioning of financial networks. We do this in two steps. First, we use previously unreleased data to show that focusing on the economic functions performed over time by the different institutions exchanging funds in an interbank market can be informative, even if the underlying topological structure of their relations remains constant. Second, a set of alternative artificial histories are generated and stress-tested by using real data as a calibration base, with the aim of performing counterfactual welfare comparisons among different topological structures.     

Managerial and program inefficiency for European meat manufacturing firms: A dynamic multidirectional inefficiency analysis approach

This paper proposes a dynamic multidirectional inefficiency analysis approach within the context of Data Envelopment Analysis to measuring input- and investment-specific managerial and program inefficiency for groups of firms characterized by different technologies. Dynamic managerial inefficiency refers to the distance to the firms’ group-specific dynamic frontier of best practices, and dynamic program inefficiency measures the difference between the group-specific dynamic frontier and the pooled dynamic frontier. The empirical application focuses on panel data of large meat processing firms in Eastern, Western and Southern Europe over the period 2005–2012. The results show that Eastern European firms have the highest dynamic managerial inefficiency for all inputs, but have the smallest values for dynamic program inefficiency. Western European firms perform worst in terms of program inefficiency for all inputs, while Southern European firms are the best with regard to dynamic managerial inefficiency. The results also reveal that regardless the dynamic inefficiency dimension considered, investments is the most inefficient input, followed by labor, and materials.     

Contract design in dynamic agency: An experimental analysis

This paper reports results from an experiment studying contract design in a dynamic 2‐period agency relationship with unobservable effort. A deferred compensation contract is theoretically optimal—it places all incentives on the outcomes in the second period. Observed contract choices offer a substantial part of the incentives for the high outcome in the first period suggesting a strong preference for timely rewards. Information about theoretically optimal bonuses and effort decisions shifts contract choices towards a deferred compensation contract. This contract structure is more profitable for principal participants.     

基于分形方法的城市路网交通形态分析

城市路网交通形态的合理与否是关系到城市交通顺畅与否的基础性要素.运用分形理论及其方法,针对路网覆盖性与连通性,分别建立相应的分形模型,进而通过探讨模型分形意义下的维数,达到分析城市路网交通形态的目的.最后,通过对某城市局部路网的实例分析,验证了将分形方法引入城市路网交通形态分析的可行性.     

A component model for dynamic correlations

We propose a model of dynamic correlations with a short- and long-run component specification, by extending the idea of component models for volatility. We call this class of models DCC-MIDAS. The key ingredients are the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model replacing the original DCC dynamics with a component specification and the Engle et al. (2006) GARCH-MIDAS specification that allows us to extract a long-run correlation component via mixed data sampling. We provide a comprehensive econometric analysis of the new class of models, and provide extensive empirical evidence that supports the model’s specification.     

A cognitive hierarchy model of learning in networks

This paper proposes a method for estimating a hierarchical model of bounded rationality in games of learning in networks. A cognitive hierarchy comprises a set of cognitive types whose behavior ranges from random to substantively rational. Specifically, each cognitive type in the model corresponds to the number of periods in which economic agents process new information. Using experimental data, we estimate type distributions in a variety of task environments and show how estimated distributions depend on the structural properties of the environments. The estimation results identify significant levels of behavioral heterogeneity in the experimental data and overall confirm comparative static conjectures on type distributions across task environments. Surprisingly, the model replicates the aggregate patterns of the behavior in the data quite well. Finally, we found that the dominant type in the data is closely related to Bayes-rational behavior.