Knowledge-Driven Agglomeration Processes

According to the economic geography literature, firms tend to geographically cluster when agglomeration economies exist. These are positive externalities associated with the co-location of firms within a bounded geographic area. Traditionally, the agglomerative advantages have been expressed in terms of pecuniary externalities and they have been identified as one of the key sources of the geographical clusters' competitive advantage. However, in the last years the basics of competition are changed and the ability of firms to create new knowledge is more crucial for success rather than the efficiency in production. This has shifted the attention of scholars on the role of knowledge and learning for the competitiveness and success of geographical clusters. In line with these studies, the chapter suggests that agglomeration economies are related to both pecuniary externalities and knowledge-based externalities. The latter are benefits that co-located firms can gain in terms of development of knowledge. To investigate whether knowledge-based externalities affect geographical clustering of firms, an agent-based model is developed. By using this model, a simulation analysis is carried out.

A Comparison between International Trade and R&D Collaboration Networks in the European Aerospace Sector

Do trading countries also collaborate in R&D? This is the question that, facing with a number of methodological problems, here it is dealt with. Studying and comparing the international trade network and the R&D collaboration network of European countries in the aerospace sector, social network analysis offers a wide spectrum of methods and criteria either to make them comparable or to evaluate its similarity. International trade is a 1-mode directed and valued network, while the EU-subsidized R&D collaboration is an affiliation (2-mode) undirected and unvalued network, and the elementary units of this latter are organizations and not countries. Therefore, to the aim to make these two networks comparable, this paper shows and discusses a number of methodological problems and solutions offered to solve them, and provides a multi-faceted comparison in terms of various statistical and topological indicators. A comparative analysis of the two networks structures is made at aggregate and disaggregate level, and it is shown that the common centralization index is definitively inappropriate and misleading when applied to multi-centered networks like these, and especially to the R&D collaboration network. The final conclusion is that the two networks resemble in some important aspects, but differ in some minor traits. In particular, they are both shaped in a core-periphery structure, and in both cases important countries tend to exchange or collaborate more with marginal countries than between themselves.

Network Analysis for Economics and Management Studies

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

A Methodology to Measure the Hierarchical Degree of Formal Organizations

Hierarchy is a fundamental phenomenon in management and organization science, a phenomenon which has marked the evolution of human societies over centuries. Among the many studies on this issue, the ones that adopt a formal approach of investigation are mainly based on social network analysis. Following this line, in this work we focus on organization distribution of formal direct authority in stylized, pure hierarchical archetypes. Past research, analyzing the share of asymmetric links in out-tree topologies, was not able to distinguish among different types of out-trees. Indeed since the out-trees can differ under substantial structural features, in order to measure the degree of hierarchy it is necessary to employ indicators of power concentration and distribution. Results show that the purest archetype of hierarchy is the star form, and not the typical org chart. Further, ceteris paribus, an organization with more hierarchical levels is less and not more hierarchical than an organization with fewer levels. Moreover, power tend to concentrate in lower levels, and especially into the penultimate one.

An International Trade Comparison of Two Supposedly Different Sectors

According to modern international economics, and especially evolutionary economic geography, a country industry characteristics influence the structure of its international trade. Following this view, this chapter moves from the following basic research issue: if two sectors are very different according to market, economic and technological aspects, should we expect that its corresponding international trade networks are as well markedly different? Aerospace and Common Earth Materials seem quite different in those respects, and thus, they are good candidates to explore that research issue. Its comparison allowed to evidence and discuss some methodological problems in applying social network analysis, and especially in using it to compare different networks. In particular, it is underlined the difficulty to handle valued networks when value variance is very high, and to combine three groups of indicators: simple, hierarchy focused, and strictly topological. The comparative analysis employed 32 indicators either at network or sub-network level, like for core-periphery analysis, which indicate clear and marked diversity only in terms of hierarchical degree and topological aspects. A first conclusion is that the two examined trade networks are following a similar path and, excepted for few indicators, they seem to be rather similar even at a deeper structural level. Hence, one (or more) of three implications can be drawn: 1) the global value networks corresponding to the two sectors are not so markedly different; 2) they are substantially different but such a diversity does not produce a significant difference in terms of international trade networks; 3) there are some methodological problems that prevent differences to be evidence and require a more refined and modified comparison. A second conclusion is that trade patterns of both sectors are rather unstable.

Agent-Based Simulation Modeling

Computer simulations are a powerful tool for scientific research, but lack an accepted methodology for their use, and consequently their results are generally received with skepticisms. This chapter proposes a methodological approach allowing to formally unify the treatment of “traditional” quantitative phenomena with that of phenomena from economics or biology that prevent a universal adoption of data-centered methods. We propose to adopt the explanation as the basic unit of knowledge, which is able to cover all possible cases. From this assumption, we can derive the conclusion that simulation models fail to deliver their full potential as scientific investigative tool because their implementations lack crucial details on the intermediate steps producing simulation results.

NK Simulation Modeling

Launched and developed primarily by Kauffman from the end of sixties, NK simulation modelling candidates for capturing networks dynamics. Grounded in reference to biological networks, it has aroused a grate and durable interest in economics and management sciences too. This methodology is split into a version focused on studying proper Boolean networks dynamics, whose trajectories are substantially conditioned by Boolean functions, and a (much more frequented) version focused on systems co-evolutionary paths driven by the search for optimizing its fitness value. Besides the unquestionable value of Kauffman's work for the theoretical implications on evolutionary biology and the strong interest for economics and management sciences, in this chapter failures and limitations of both NK modelling versions are discussed. In particular, it is shown that as applications try to be more realistic, this modeling becomes hardly treatable from a computational point of view. On the other hand, it is underlined that, especially the fitness landscape version, NK simulation modelling is very useful to show general aspects of system's dynamics, and the impossibility to find general optima (excepted for very special and unrealistic cases). This result sounds a sharp criticism to general economic equilibrium, and it is perfectly consistent with Simon's contributions.

On the Relationships between Connection Modes and Workgroup Performance

Notwithstanding the central place covered inside organization science and the economic theory of the firm, organization design theory still lacks sound building blocks concerning the effects that some fundamental variables have on workgroup performance. In this chapter a contribution to fill in this gap is given with reference to the relationships between connection modes and performance. In particular, through an agent-based simulation model a number of experiments have been done respect to the moderating role played by group size and task complexity. Results confirm current (but not really scientific) knowledge, and bring forth our understanding of these fundamental (and mostly nonlinear) relationships. Among the main results, it can be underlined that the best combinations between connection modes, task complexity, and workgroup size occur when complex tasks are connected by mutual adaptation and run by a small number of agents, or when less complex tasks are connected by parallel or sequential interdependence and performed by a large number of agents. Moreover, when a modules volume to be worked out is heterogeneous in terms of connection modes between module's tasks, and thus, a multi-mode group should be issued, respect to the corresponding choice of issuing specialized groups there is a general decrease in efficacy.

Innovations and Organizational Structures

This chapter presents a novel model to represent the relative results that different innovation strategies can be expected to attain in respect of technological spaces defined by different complexity levels. The model representing complexity is based on Kauffman's fitness landscape (known, also, as NK fitness landscape). However, contrary to the original proposal, we used a functional and deterministic representation of complexity defined over a real-valued space, replacing a stochastic and statistical-based definition of complexity in binary-valued spaces. The search strategies proposed are inspired to hierarchical organizations like companies. Low-level employees are assumed to have a full (technical) knowledge of a sub-set of the organization's functions, but lack the global vision necessary to assess the impact of a change on the overall organization's performance. Higher-level management has the task to weight different alternative proposals and select the best one for the benefit of the company as a whole, even though they are not able to explore directly technical solutions. The results confirm the results already known in the literature, though in much clearer and robust way, and suggest a large number of possible extensions.

The Role of Knowledge Heterogeneity in Industrial Clusters Knowledge Dynamics

In high-tech industrial clusters as the aerospace most collaborations for innovations are highly knowledge-specific and form a (relatively dense) knowledge network. With reference to the case of the aerospace industrial cluster of the Lazio Region, in this work we study the network dynamics of its core organizations (firms, universities, and research centers). By applying the methodology of NK simulation modeling, we explore what happens lacking the initial contribution of knowledge provided by universities and research centers. Further, we investigate the effects of the introduction of minimum requisites in terms of knowledge heterogeneity and knowledge amount. We show that, within a general favorable condition of activation rules, trajectories are quite short and knowledge dynamics is sensitive to the requisite of knowledge heterogeneity, when it is set-up around half of its potential range. We will conclude that, despite some interesting results like the ones we have found and many others that could be discovered, this methodology has substantial failures mostly due to its requisites of computational burden and topological and behavioral invariance, which makes it hardly applicable and scarcely informative into empirical analysis of phenomena within economics and management sciences.