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.     

Social networks and labour productivity in Europe: an empirical investigation

This paper uses firm-level data recorded in the Amadeus database to investigate the distribution of labour productivity in different European countries. We find that the upper tail of the empirical productivity distributions follows a decaying power-law, whose exponent α is obtained by a semi-parametric estimation technique recently developed by Clementi et al. [Physica A 370(1):49–53, 2006]. The emergence of “fat tails” in productivity distribution has already been detected in Di Matteo et al. [Eur Phys J B 47(3):459–466, 2005] and explained by means of a model of social network. Here we show that this model is tested on a broader sample of countries having different patterns of social network structure. These different social attitudes, measured using a social capital indicator, reflect in the power-law exponent estimates, verifying in this way the existence of linkages among firms’ productivity performance and social network.     

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in “new” or remanufactured products. However, this option is partly limited by a firm's ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology.     

Social support among heterogeneous partners: an experimental test

This paper studies how dyadic social support is affected by heterogeneity of the partners. We distinguish heterogeneity with respect to three parameters the likelihood of needing support; the benefits from receiving support; and the costs of providing support. Hypotheses are based on a game-theoretic analysis of an iterated support game. First, we predict that heterogeneity in one of the parameters hampers social support. Second, we predict that under heterogeneity with respect to two of the parameters, support is most likely if there is a specific heterogeneous distribution such that heterogeneity in one parameter ‘compensates’ for heterogeneity in the other parameter. If there is no compensation social support is even more hampered. The hypotheses have been tested by experimental data with a mixed within-subject, between-subject design. The data gives support to the hypotheses.     

Models for data transmission delay in cable networks

Cable networks have been upgraded in recent years to support bidirectional traffic rather than just unidirectional television signals. Thus, it has become possible to use cable networks for internet browsing and telephony. In this paper, we propose delay models to determine how this bidirectional traffic is best handled and to evaluate the resulting performance of the network, notably its delay properties. It is shown that these models, variations on the repairman problem and the bulk service queue, lead to actual improvements in the data transmission schedules for cable networks. Moreover, these models can be combined in order to arrive at approximations for the average packet delay. Our theoretical calculations are backed up by simulations.     

Versatile networks in combining qualitative and quantitative data

Following a series of special volumes on the DISC conferences in the past, this themed issue focuses on versatile network forms particularly in combining qualitative and quantitative data. This special issue also celebrates the second publication born out of the DISC conferences under the Quality and Quantity.     

Organized crime and public spending: a panel data analysis

The aim of this paper is to investigate, empirically, what components of public spending imply a decreasing effect on organized crime and what components create opportunities for organized crime, discussing also the role of government efficiency. Using a panel data analysis, the results show a strikingly consistent pattern for the EU Member States. Organized crime mainly operates in the distribution of government spending for local public goods and public provision of private services. There is a decreasing effect on organized crime of the public expenditure devoted to education and social policy. Government efficiency in public spending is beneficial to limit the opportunities of the organized crime.     

Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field

Quality & Quantity - Machine learning algorithms and artificial neural networks promise a new and powerful approach for making better and more transferable predictions in global conflict...     

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.     

数学学习方式的解析

中学生在常规学习中做数学课堂笔记存在很多问题,但要学好数学,必须养成良好的学习习惯,特别要注意认真记好、用好课堂笔记。文章就如何做好数学课堂笔记提出了一些思路和具体的操作策略。     

Gaussian clustering and jump-diffusion models of electricity prices: a deep learning analysis

Decisions in Economics and Finance - We propose a deep learning-based methodology to investigate the complex dynamics of electricity prices observed in power markets. The aims are: (a) to process...     

A Bayesian approach to experimental analysis: trading in a laboratory financial market

We employ a Bayesian approach to analyze financial markets experimental data. We estimate a structural model of sequential trading in which trading decisions are classified in five types: private-information based, noise, herd, contrarian and irresolute. Through Monte Carlo simulation, we estimate the posterior distributions of the structural parameters. This technique allows us to compare several non-nested models of trade arrival. We find that the model best fitting the data is that in which a proportion of trades stems from subjects who do not rely only on their private information once the difference between the number of previous buy and sell decisions is at least two. In this model, the majority of trades stem from subjects following their private information. There is also a large proportion of noise trading activity, which is biased towards buying the asset. We observe little herding and contrarianism, as theory suggests. Finally, we observe a significant proportion of (irresolute) subjects who follow their own private information when it agrees with public information, but abstain from trading when it does not.     

Social networks,employment and worker discouragement: Evidence from South Africa

Social networks are increasingly being recognized as having an important influence on labour market outcomes, since they facilitate the exchange of job related information. Access to information about job opportunities as well as perceptions about the buoyancy of the labour market depend critically on the social structures and the social networks to which labour market participants belong. In this paper, we examine the impact of information externalities generated through network membership on labour market status. Using Census data from South Africa, a country characterized by high levels of unemployment and worker discouragement, we adopt an econometric approach that aims to minimise the problems of omitted variable bias that have plagued many previous studies of the impact of social networks. Our results suggest that social networks may enhance employment probabilities by an additional 3–12%, and that failure to adequately control for omitted variables would lead to substantial over-estimates of the network co-efficient. In contrast, the impact of social networks on reducing worker discouragement is much smaller, at between 1 and 2%.     

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.     

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.     

家庭理性的社会均衡人口规模与社会最优人口规模的博弈分析

发达国家与发展中国家的人口增长有着明显的差异，自由生育的发展中国家，由于人口过快增长拖累了经济发展，从而无法摆脱贫困，无法摆脱“人口困境”。同样，发达国家的人口落入了另一个困境，每个家庭都从理性出发，都从追求家庭效用最大化出发而陷入了“公用品私人自愿供给”的困境。这种人口困境是一种“纳什陷阱”，运用博弈论的分析方法得出了这样的结论：只有国家实施“相机抉择”的计划生育政策，才能实现社会最优人口规模。     

Tackling selection bias in sentencing data analysis: a new approach based on a scale of severity

For reasons of methodological convenience statistical models analysing judicial decisions tend to focus on the duration of custodial sentences. These types of sentences are however quite rare (7% of the total in England and Wales), which generates a serious problem of selection bias. Typical adjustments employed in the literature, such as Tobit models, are based on questionable assumptions and are incapable to discriminate between different types of non-custodial sentences (such as discharges, fines, community orders, or suspended sentences). Here we implement an original approach to model custodial and non-custodial sentence outcomes simultaneously avoiding problems of selection bias while making the most of the information recorded for each of them. This is achieved by employing Pina-Sánchez et al. (Br J Criminol 59:979–1001, 2019) scale of sentence severity as the outcome variable of a Bayesian regression model. A sample of 7242 theft offences sentenced in the Crown Court is used to further illustrate: (a) the pervasiveness of selection bias in studies restricted to custodial sentences, which leads us to question the external validity of previous studies in the literature limited to custodial sentence length; and (b) the inadequacy of Tobit models and similar methods used in the literature to adjust for such bias.     

Learning about learning in games through experimental control of strategic interdependence

We report results from an experiment in which humans repeatedly play one of two games against a computer program that follows either a reinforcement or an experience weighted attraction learning algorithm. Our experiment shows these learning algorithms detect exploitable opportunities more sensitively than humans. Also, learning algorithms respond to detected payoff-increasing opportunities systematically; however, the responses are too weak to improve the algorithms' payoffs. Human play against various decision maker types does not vary significantly. These factors lead to a strong linear relationship between the humans' and algorithms' action choice proportions that is suggestive of the algorithms' best response correspondences.