Statistical mechanics of coevolving spin system

We propose a statistical mechanics approach to a coevolving spin system with an adaptive network of interactions. The dynamics of node states and network connections is driven by both spin configuration and network topology. We consider a Hamiltonian that merges the classical Ising model and the statistical theory of correlated random networks. As a result, we obtain rich phase diagrams with different phase transitions both in the state of nodes and in the graph topology. We argue that the coupling between the spin dynamics and the structure of the network is crucial in understanding the complex behavior of real-world systems and omitting one of the approaches renders the description incomplete

Phenomenological Models of Socio-Economic Network Dynamics

We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between 'similar' nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence.Comment: 11 pages, 8 figure

Determinants of University Spin-Offs’ Growth: Do Socioeconomic Networks and Support Matter?

University spin-offs (USOs), as a type of entrepreneurial firms, face the challenge of obtaining sufficient resources to realize perceived business opportunities. USOs are vulnerable to many obstacles in this endeavor, particularly obstacles related to a lack of entrepreneurial knowledge (skills). Support such as office facilities, loan, and business coaching provided by incubator organizations, may help USOs to overcome obstacles. On the other hand, USOs may also overcome the lack of resources by participating in networks of supportive relationships. Social networking by USOs, including its spatial dimension, is not well understood. For instance, it is still not known how universities as a main source of knowledge contribute to the knowledge needs of nearby USOs; similarly, the spatial layout of knowledge relations of USOs has remained virtually unknown. This paper attempts to fill this knowledge gap. Our conceptual model of early growth of USOs, in terms of knowledge needs and fulfilment, is based on resource-based theory and social network theory. In this paper, we assume that USOs’ embeddedness in a network of ties is an important source of variation in the acquisition of knowledge resources. We argue that, aside from support from incubation organizations, USOs that maintain networks rich in bridging or boundary-spanning ties with knowledge institutions/actors are better-off compared with USOs that don’t employ such ties. We focus on the role of local institutions, particularly the university, as a source of knowledge. Our assumptions are tested on the basis of a sample of academic spin-offs of TU Delft, the Netherlands. The results from regression modeling are expected to support the embeddedness hypothesis and to produce new insights about the link between USOs’ social networks, the acquisition of knowledge and survival and growth.

Socio-economical dynamics as a solvable spin system on co-evolving networks

We consider social systems in which agents are not only characterized by their states but also have the freedom to choose their interaction partners to maximize their utility. We map such systems onto an Ising model in which spins are dynamically coupled by links in a dynamical network. In this model there are two dynamical quantities which arrange towards a minimum energy state in the canonical framework: the spins, s_i, and the adjacency matrix elements, c_{ij}. The model is exactly solvable because microcanonical partition functions reduce to products of binomial factors as a direct consequence of the c_{ij} minimizing energy. We solve the system for finite sizes and for the two possible thermodynamic limits and discuss the phase diagrams.Comment: 5 pages 3 fig

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published