Sonja Novkovic

R&D, Innovation and Networking: Strategies for Cooperative Survival

This paper examines R&D and innovation patterns of firms in a mixed industry as a possible explanation of relative scarcity of worker cooperatives in market economies. In a set of simulations of a dynamic evolving industry, cooperatives and investor-owned firms are contrasted with regard to their attitudes toward research and development in their controlling inputs, and the non-controlling inputs. We investigate the conditions under which networking according to the principle of cooperation among cooperatives helps cooperative firms maintain significant market share in the industry. It turns out that forming close networks is a good policy for cooperative survival, even when investor-owned firms have the innovation rate advantage.

A Genetic Algorithm Simulation of a Transition Economy: An Application to Insider-Privatization in Croatia

A genetic algorithm simulation is applied to a model of privatization in a transition economy. Bounded-rational agents, learning by doing in a changing economic environment, are presented as a population of artificial adaptive agents. The paper examines the comparative performances of three alternative forms of genetic algorithm – the simple GA, PGA with election and EGA with elite selection. The latter version proved to be more robust than the alternatives.

The minimal deceptive problem revisited: the role of “genetic waste”

Abstract Genetic algorithm (GA) is used in a number of fields for effective search of complex spaces, or as a learning algorithm. Based on natural genetics, it feeds on its ability to process large amounts of information very quickly. The performance of a GA is satisfactory in most cases, in that it finds solutions in the neighborhood of the global optimum. Some problems, however, are known to be GA-hard and deceive the GA to converge to sub-optimal solutions. We devise a version of GA through the addition of the “genetic waste”, based on the perceived role of “nonsense codons” (or “noncoding segments”), which alleviates the problem by allowing sufficient diversity in the population. This modification allows the algorithm to change the parameters in each run without external interference. Furthermore, the parameters are independent of the fitness values and are thereby applicable to a wide class of problems. Using Goldbergs [10] minimal deceptive problem, we devise a simple method for setting the initial parameters of a genetic algorithm based on the role of nonsense codons in natural genetics. “Genetic waste” provides random parameters throughout the simulations, enabling the abandonment of trial and error search for the optimal combination of initial parameters and thereby adding credibility to the genetic algorithm as a tool of adaptive search.

Staying True to Co-Operative Identity: Diagnosing Worker Co-Operatives for Adherence to their Values

This chapter contributes to the discourse on the impact of employee participation in organisations. Using worker co-operatives as special cases of participatory firms, we discuss the role of values in organisations and their importance in a business context. We devise and apply the CoopIndex diagnostic tool as a method of assessment of the ‘health’ of an organisation whose members aspire to align co-operative management with the application of the co-operative principles and values.