Bayesian learning in social networks

We extend the standard model of social learning in two ways. First, we introduce a social network and assume that agents can only observe the actions of agents to whom they are connected by this network. Secondly, we allow agents to choose a different action at each date. If the network satisfies a connectedness assumption, the initial diversity resulting from diverse private information is eventually replaced by uniformity of actions, though not necessarily of beliefs, in finite time with probability one. We look at particular networks to illustrate the impact of network architecture on speed of convergence and the optimality of absorbing states. Convergence is remarkably rapid, so that asymptotic results are a good approximation even in the medium run.     

Returns from social capital in open source software networks

Open Source Software projects base their operation on a collaborative structure for knowledge exchange in the form of provision or reception of information, expertise and feedback on the creation of source code. Here, we address the direction of these knowledge flows among projects throughout social networks and their impact on project success. We identify the roles of membership or contribution that individuals play within projects. We found that connections through contributors who bring their knowledge to the project, improve project success, and that connection through members, who transfer their knowledge towards other projects, enhance project success. Finally, we found that ties through shared membership and contributions hamper project success. The analysis of knowledge flows and their impact on project success imply a translation of returns from investment in social capital, where investment takes the shape of knowledge flows and the returns mean the projects’ diffusion over the network.

A simple model of homophily in social networks

Biases in meeting opportunities have been recently shown to play a key role for the emergence of homophily in social networks (see Currarini et al., 2009). The aim of this paper is to provide a simple microfoundation of these biases in a model where the size and type-composition of the meeting pools are shaped by agents׳ socialization decisions. In particular, agents either inbreed (direct search only to similar types) or outbreed (direct search to population at large). When outbreeding is costly, this is shown to induce stark equilibrium behavior of a threshold type: agents “inbreed” (i.e. mostly meet their own type) if, and only if, their group is above certain size. We show that this threshold equilibrium generates patterns of in-group and cross-group ties that are consistent with empirical evidence of homophily in two paradigmatic instances: high school friendships and interethnic marriages.     

Diffusion in complex social networks

This paper studies how a behavior spreads in a population. We consider a network of interacting agents whose actions are determined by the actions of their neighbors, according to a simple diffusion rule. We find, using a mean-field approach, the threshold for the spreading rate above which the behavior spreads and becomes persistent in the population. This threshold crucially depends on the connectivity distribution of the social network and on specific features of the diffusion rule.     

On the formation of interaction networks in social coordination games

There are many situations where two interacting individuals can benefit from coordinating their actions. We examine the endogenous choice of partners in such social coordination games and the implications for resulting play. We model the interaction pattern as a network where individuals periodically have the discretion to add or sever links to other players. With such endogenous interaction patterns we see multiple stochastically stable states of play, including some that involve play of equilibria in the coordination game that are neither efficient nor risk-dominant. Thus the endogenous network structure not only has implications for the interaction pattern that emerges, but it also has a significant impact on the play in the coordination game relative to what would arise if the same interaction network were exogenous.     

Social cooperatives,social welfare associations and social networks

We use an original data-set to study how participation in two types of non-profit organizations – i.e. social welfare associations and social cooperatives – affects individual social capital, understood as a network of cooperative relationships. Participation in both the types of organization allows members to start new social relations. However, social welfare associations seem to play a significantly greater role in the development of volunteers’ social capital, favouring the creation of weak ties that are used to exchange information and advice, and offering the opportunity to establish stronger ties entailing concrete mutual support. Within social cooperatives, workers appear to develop their individual social capital to a greater extent than volunteers. Our results suggest that the composition of the workforce, the depth of members’ involvement in the organization’s activities and the human resources strategies adopted by the management influence the creation of cooperative relations through on-the-job interactions.     

Grasping the potential of online social networks for foresight

Online Social Networks (OSNs) have gained unprecedented popularity in recent years. OSNs facilitate the interaction among members by providing a dynamic/multimodal platform which enables discussions, sharing of multimedia content, organisation of events, etc. These networks comprise millions of members from all continents and from all age groups — although the younger generation is more prominent. OSN dynamics and inherent patterns of operation are being investigated by academia as a means of studying, for instance, ICT-enhanced social change. Industry uses them to detect new commercial trends and establish marketing strategies.We believe that the huge size of OSNs, the broad and versatile thematic topics and the fact that most users are youngsters made these new modalities of large-scale interaction also worth investigating in regard to the study of the future or foresight. In this paper, we discuss the relevance of OSNs for three objectives of foresight methods, namely creativity, expertise and collective intelligence.First, we argue that OSNs can be regarded as a tool to enhance creativity through the unprecedented modalities of communication and interaction they offer.Second, we propose OSNs as an expert tool to detect emerging changes in social behaviour. We assume that the recorded exchanges of information and thoughts between participants in forums offers an under-exploited source of information for detecting new social trends. The value of this information may be amplified by evaluating it in conjunction with other OSN data, such as member profiles, behavioural patterns or list of contacts or friends. In this way, emerging social trends could be detected. Similar approaches are already used in market research and could be transferred to foresight.Third, we consider OSNs to be a means of aligning individual thinking and fostering collective or “collaborative” intelligence for a whole range of possible goals in the future.For each of these three objectives, the theoretical foundations are complemented with some case studies. Given the novelty of the OSN phenomenon and its unexplored potential in many fields, the authors aim to trigger thinking and discussion on the potential application of this emerging phenomenon within foresight, rather than to offer a vademecum on the use of OSNs for foresight activities.     

Regional social capital: Embeddedness, innovation networks and regional economic development

Technology is a necessary but not a sufficient condition for regional economic development. Regional innovation networks transform technology into competitiveness of firms and thus contribute to economic development. Intangible assets, such as social capital, decide how effective regional innovation networks function. Differences in regional social capital thus help explain regional differences in economic development. Regional social capital originates from the embeddedness of firms in regional webs of social relations. The norms, values and customs of these networks facilitate collaboration for mutual benefit. As innovation is increasingly a network effort, embeddedness and social capital also help explain how and why networks of innovating companies are successful, as the case study of the Stimulus Cluster Scheme shows.     

The structure of information networks

We develop a strategic model of information acquisition in networks where agents pay for all the pieces of information they acquire, including those through indirect links. The cost of information depends on the distance it traverses in the network. We consider two possibilities in this context: (1) costs increasing with distance, and (2) costs decreasing with distance. The paper also examines situations where it is more expensive to acquire information of higher value. We show that there is almost no divergence between the efficient and Nash equilibrium information architectures. We then study the effect of decay in networks where information through longer paths is cheaper. Finally, we also examine a model with costly link formation that combines both types of cost related assumptionsWe are grateful to Hans Haller, Rob Gilles, Susanne Maria Schmidt, Sumit Joshi, Georg Erber, Beth Allen, Mark Machina, Bob Martin, Johanna Francis, Cheryl Long, Raja Kali, Kaz Miyagiwa, Micheal Kosfeld, Bibhudutta Panda and two anonymous referees for helpful suggestions. The paper has further benefited from the comments of participants at Game Theory and Applications Mumbai 2003, Royal Economic Society Meetings 2003, SED 2004 and GAMES 2004. Sudipta Sarangi acknowledges the hospitality of DIW Berlin where a part of this research was carried out. Rajgopal Kannan acknowledges the support of NSF grants IIS-0329738 and IIS-0312632     

Reciprocity in evolving social networks

We study the viability of conditional cooperation in a dynamically evolving social network. The network possesses the small world property, with high clustering coefficient but low characteristic path length. The interaction among linked individuals takes the form of a multiperson prisoners’ dilemma, and actions can be conditioned on the past behavior of one’s neighbors. Individuals adjust their strategies based on performance within their neighborhood, and both strategies and the network itself are subject to random perturbation. We find that the long-run frequency of cooperation is higher under the following conditions: (i) the interaction radius is neither too small nor too large, (ii) clustering is high and characteristic path length low, (iii) the mutation rate of strategies is small, and (iv) the rate of adjustment in strategies is neither too fast nor too slow.

Social capital, inclusive networks, and economic performance

Empirical studies show that the relationship between social capital and economic performance is ambiguous. The paper points to the potential trade-off between the sustainability of self-enforcement and the magnitude of gains from trade in social networks as an explanation. Based on an infinitely repeated multi-player prisoners’ dilemma it is shown how self-enforcement of cooperation within a network is influenced by its inclusiveness, its communication capacity, and the complexity of the exchange setting. The paper shows that inclusive social capital can combine both low enforcement costs and high gains from trade even in a complex exchange setting.     

Social networks and industrial geography

In many industries, production resides in a small number of highly concentrated regions; for example, several high tech industries cluster in Silicon Valley. Explanations for this phenomenon have focused on how the co-location of firms in an industry might increase the efficiency of production. In contrast, this article argues that industries cluster because entrepreneurs find it difficult to access the information and resources they require when they reside far from the sources of these valuable inputs. Since existing firms often represent the largest pools of these important factors, the current geographic distribution of production places important constraints on entrepreneurial activity. As a result, new foundings tend to arise in the same areas as existing ones, and hence reproduce the industrial geography. In support of this thesis, the article reviews empirical evidence from the shoe manufacturing and biotechnology industries.JEL Classification: L11, M13, R30Adapted from a plenary talk delivered at the 8 ^th annual meetings of the International Schumpeter Society in Gainesville, FL. Constança Esteves and Lee Fleming provided comments useful to developing this written version.     

Will social networks become less important

This letter finds that the social networks are more important for the high-income class, and less important for the low-income class on income increases. Thus, research on the economic influence of social networks should notice the endogeneity of the social network functions of different classes.     

Conformism and diversity under social learning

Summary. When there are competing technologies or products with unknown payoffs an important question is which technology will prevail and whether technologies with different payoffs can coexist in the long run. In this paper, we use a social learning model with local interactions to study this question. We show that the adoption of technologies as well as the prospects of conformism/diversity depend crucially on the nature of interaction between individuals and the heterogeneity of preferences in a society. Received: May 10, 1999; revised version: February 4, 2000     

Voting in small networks with cross-pressure

We present a model of participation in elections in small networks, in which citizens suffer from cross-pressures if voting against the alternative preferred by some of their social contacts. We analyze how the existence of cross-pressures may shape voting decisions, and so, political outcomes; and how parties may exploit this effect to their interest. We characterize the strong perfect equilibria of the game and show that, in equilibrium, the social network determines which party wins the election. We also show that to dispose of the citizens better connected in the network with the other faction is not a guarantee to win the election.      

Risky business: Institutions vs. social networks in FDI

Political economy scholarship on foreign direct investment (FDI) emphasizes variation in host country political risk but overlooks variation in investors' sensitivity to political risk. We show that relational contracting, relationship‐based contract enforcement, is more efficient for high‐risk, human capital‐intensive activities for which the costs of writing legally enforceable contracts are prohibitive. We disaggregate FDI into two distinct varieties: mergers and acquisitions (M&A) and venture capital (VC). We propose that VC flows are less sensitive to host institutions but correlate strongly with skilled migrant networks that monitor compliance and impose reputational costs. Our empirical analysis of dyadic VC and M&A flows covers over 100 countries during 1980–2009. We address other mechanisms through which migrant networks facilitate FDI and verify our results hold at the country‐industry level. These findings suggest that relational contracting facilitates global integration of dynamic, knowledge‐intensive industries even when formal institutions are weak.     

Does social capital determine innovation? To what extent?

This paper deals with two questions: Does social capital determine innovation in manufacturing firms? If it is the case, to what extent? To deal with these questions, we review the literature on innovation in order to see how social capital came to be added to the other forms of capital as an explanatory variable of innovation. In doing so, we have been led to follow the dominating view of the literature on social capital and innovation which claims that social capital cannot be captured through a single indicator, but that it actually takes many different forms that must be accounted for. Therefore, to the traditional explanatory variables of innovation, we have added five forms of structural social capital (business network assets, information network assets, research network assets, participation assets, and relational assets) and one form of cognitive social capital (reciprocal trust). Based on the survey data administered from April to June 2000 to 440 manufacturing firms of diverse industries in a region in the southwest of Montréal, we have found that 68.5% of the firms have developed product or process innovations during the 3 years preceding the survey. Assuming that innovation is not a discrete event but a complex process, we have modeled the decision to innovate as a two-stage decision-making process: in the first stage, the firms deal with the decision about whether to innovate or not whereas, at the second stage, the firms that have decided to innovate must make a decision about the degree of radicalness of the innovation to undertake. In a context where empirical investigations regarding the relations between social capital and innovation are still scanty, this paper makes contributions to the advancement of knowledge in providing new evidence regarding the impact and the extent of social capital on innovation at the two decision-making stages considered in this study. Regarding the decision to innovate or not that firms must initially make, we have provided strong evidence that diverse forms of social capital influence this decision and, more importantly, that marginal increases in social capital, especially in social capital taking the forms of participation assets and relational assets, contribute more than any other explanatory variable to increase the likelihood of innovation of firms. As for the decision to be made at the second stage concerning the magnitude of radicalness to bring in the development of new product or process innovations, this paper contributes to the advancement of knowledge by supplying the strongest evidence that diverse forms of social capital determine the radicalness of innovation, and more importantly, that social capital taking the form of research network assets contributes more than any other explanatory variable to explain the radicalness of innovation. The second variable that exerts the strongest impact on the radicalness of innovation is the number of different advanced technologies employed by firms for production.     

Use of social networks in banking: a study in the Czech Republic

Social networks are becoming more and more important nowadays. Apart from regular users of the Internet, companies have started to use them in their business. Their potential is that a great number of people, future clients of big companies, use them on a daily basis. Thus, the aim of this article is to analyse the use of social networks in the banking sector of the Czech Republic, particularly with respect to customer support. The methods of this study include common data analysis and professional analysis. Common data analysis consists of the evaluation of public data from social networks and web pages of the selected bank institutions. The professional analysis is conducted by paid professional analytical tools that provide more detailed statistics. The findings of the study show that social networks are now well-established tools for the Czech banks to attract both their current and potential clients. The Czech banks seem to care about their clients by trying to meet their needs. Customer care and services provided, however, differ with type of bank.     

Social media networks,fake news,and polarization

We study how the structure of social media networks and the presence of fake news affects the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and exhibit bounded rationality. Key to the analysis is the presence of internet bots: agents in the network that spread fake news (e.g., a constant flow of biased information). We characterize how agents’ opinions evolve over time and evaluate the determinants of long-run misinformation and polarization in the network. To that end, we construct a synthetic network calibrated to Twitter and simulate the information exchange process over a long horizon to quantify the bots’ ability to spread fake news. A key insight is that significant misinformation and polarization arise in networks in which only 15% of agents believe fake news to be true, indicating that network externality effects are quantitatively important. Higher bot centrality typically increases polarization and lowers misinformation. When one bot is more influential than the other (asymmetric centrality), polarization is reduced but misinformation grows, as opinions become closer the more influential bot’s preferred point. Finally, we show that threshold rules tend to reduce polarization and misinformation. This is because, as long as agents also have access to unbiased sources of information, threshold rules actually limit the influence of bots.