This paper is dedicated to Philip J. Stone III, who first put me online in 1965.

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85 % accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization. Author Keywords Social media, social networks, relationship modeling, ties,

Computational and mathematical organization theory is an inter-disciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization’s position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally.

We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence of social interactions; the baseline probability of working together is 0.93 % at the block level compared to 0.51 % at the block group level (a collection of ten contiguous blocks). We also provide evidence as to which types of matches between individuals result in greater levels of referrals. These findings are robust to the introduction of detailed controls for socio-demographic characteristics and block group fixed effects, as well as across various specifications intended to address sorting and housing market rather than labor market referrals. Further, our estimated effects have a significant impact on a wide range of labor market outcomes more generally.

This paper accounts for the observed increase in unemployment duration relative to the unemployment rate in the U.S. over the past thirty years, typified by the record low level of short-term unemployment. We show that part of the increase is due to changes in how duration is measured, a consequence of the 1994 Current Population Survey redesign. Another part is due to the passage of the baby boomers into their prime working years. After accounting for these shifts, most of the remaining increase in unemployment duration relative to the unemployment rate is concentrated among women, whose unemployment rate has fallen sharply in the last two decades while their unemployment duration has increased. Using labor market transition data, we show that this is a consequence of the increase in women's labor force attachment. # We are grateful to Giuseppe Bertola, Robert Solow, David Weiman, and participants in the Sustainable Employment Initiative conference for their comments and to Fran Horvath, Randy Ilg, Rowena Johnson, Bob McIntire, and Anne Polivka at the Bureau of Labor Statistics (BLS) for their help compiling the data. Ron Tucker of the Census Bureau and Clyde Tucker of the BLS provided useful information concerning the Current Population Survey redesign. Joydeep Roy provided valuable research assistance. Shimer acknowledges financial support from National Science Foundation grant SES-0079345 and the hospitality of the University of Chicago while part of this paper was written. Please address correspondence to shimer@princeton.edu. 1

Abstract: This paper describes mathematical models for network evolution when ties (edges) are directed and the node set is xed. Each of these models implies a speci c type of departure from the standard null binomial model. We provide statistical tests that, in keeping with these models, are sensitive to particular types of departures from the null. Each model (and associated test) discussed follows directly from one or more socio-cognitive theories about how individuals alter the colleagues with whom they are likely to interact. The models include triad completion models, degree variance models, polarization and balkanization models, the Holland-Leinhardt models, metric models, and the constructural model. We nd that many of these models, in their basic form, tend asymptotically towards an equilibrium distribution centered at the completely connected network (i.e., all individuals are equally likely to interact with all other individuals) � a fact that can inhibit the development of satisfactory tests. Keywords: triad completion, Holland-Leinhardt model, polarization, degree variance, network evolution, constructuralism

We model a labor market that includes referral networks using an agent based simulation. Agents maximize their employment satisfaction by allocating resources to build friendship networks and to adjust searchintensity. We use a local selection evolutionary algorithm, whichmaintains a diverse population of strategies, to study the adaptive graph topologies resulting from the model. The evolved networks display mixtures of regularity and randomness, as in small-world networks. A second characteristic emerges in our model as time progresses; the population loses e#- ciency due to over-competition for job referral contacts in away similar to social dilemmas such as the tragedy of the commons. Analysis reveals that the loss of global #tness is driven by an increase in individual robustness, whichallows agents to live longer by surviving job losses. The behavior of our model suggests predictions for a number of policies. Keywords--- Labor markets, referral networks, local selection, small-...

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We develop a dynamic model to study the formation of communication networks. In this model, individuals periodically make decisions concerning the continuation of existing information links and the formation of new information links, with their cohorts. These decisions trade off the costs of forming and maintaining links against the potential rewards from doing so. We analyze the long run behavior of this process of link formation and dissolution. Our results establish that this process always self-organizes, i.e., irrespective of the number of agents, and the initial network, the dynamic process converges to a limit social communication network with probability one. Furthermore, we prove that the limiting network is invariably either a wheel network or the empty network. We show in the (corresponding) static network formation game that, while a variety of architectures can be sustained in equilibrium, the wheel is the unique efficient architecture for the interesting class of paramet...