Copyright and creative innovation in China : an evolutionary approach?

China has made great progress in constructing comprehensive legislative and judicial infrastructures to protect intellectual property rights. But levels of enforcement remain low. Estimates suggest that 90% of film and music products consumed in China are ‘pirated’ and in 2009 81% of the infringing goods seized at the US border originated from China. Despite of heavy criticism over its failure to enforce IPRs, key areas of China’s creative industries, including film, mobile-music, fashion and animation, are developing rapidly. This paper explores how the rapid expansion of China’s creative economy might be reconciled with conceptual approaches that view the CIs in terms of creativity inputs and IP outputs. It argues that an evolutionary understanding of copyright’s role in creative innovation might better explain China’s experiences and provide more general insights into the nature of the creative industries and the policies most likely to promote growth in this sector of the economy.

Contemplating an evolutionary approach to entrepreneurship

This paper explores that application of evolutionary approaches to the study of entrepreneurship. It is argued an evolutionary theory of entrepreneurship must give as much concern to the foundations of evolutionary thought as it does the nature entrepreneurship. The central point being that we must move beyond a debate or preference of the natural selection and adaptationist viewpoints. Only then can the interrelationships between individuals, firms, populations and the environments within which they interact be better appreciated.

Clan-based evolutionary approach for solving control problems

This paper presents a dan-based evolutionary approach for solving control problems. Three selected control problems, viz. linear-quadratic, harvest, and push-cart problems, are solved using the proposed approach. Results are compared with those of the evolutionary programming (EP) approach. In most of the cases, the proposed approach is successful in obtaining (near) optimal solutions for these selected problems.

The FUELGEN alternative: an evolutionary approach. The architecture

This paper introduces a few architectural concepts from FUELGEN, that generates a "cloud" of reload patterns, like the generator in the FUELCON expert system, but unlike that generator, is based on a genetic algorithm. There are indications FUELGEN may outperform FUELCON and other tools as reported in the literature, in well-researched case studies, but careful comparisons have to be carried out. This paper complements the information in two other recent papers on FUELGEN. Moreover, a sequel project is outlined.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

An evolutionary approach for the motion planning of redundant and hyper-redundant manipulators

The trajectory planning of redundant robots is an important area of research and efficient optimization algorithms are needed. The pseudoinverse control is not repeatable, causing drift in joint space which is undesirable for physical control. This paper presents a new technique that combines the closed-loop pseudoinverse method with genetic algorithms, leading to an optimization criterion for repeatable control of redundant manipulators, and avoiding the joint angle drift problem. Computer simulations performed based on redundant and hyper-redundant planar manipulators show that, when the end-effector traces a closed path in the workspace, the robot returns to its initial configuration. The solution is repeatable for a workspace with and without obstacles in the sense that, after executing several cycles, the initial and final states of the manipulator are very close.

Global profit split: An evolutionary approach to international income allocation

International taxation is concerned mainly with the equitable allocation of cross-border income between countries in which income-earning activities take place. Such allocation has traditionally been governed by the arm’s-length principle, which has been interpreted as requiring a comparable transactional pricing approach. This approach assumes that each member of a multinational enterprise (MNE) group is a separate entity and that the transactions between related parties can be separated and compared with arm’s-length transactions. It has, however, proved difficult to apply comparable transactional pricing to internationally integrated businesses, especially those involving intangibles and services, and formulary apportionment has been suggested as an alternative. Essentially, formulary apportionment treats the MNE group as a single economic entity. The group’s profit is allocated to members according to a formula that reflects the particular member’s contribution to the production of that profit. A rich academic literature exists which either defends or attacks this alternative approach. The OECD and national governments have rejected formulary apportionment mainly on the ground that it violates the arm’s-length principle. This article proposes a global profit split (GPS) method for allocating international income. The GPS would allocate the global profit of an integrated business to each country in accordance with the economic contributions made by components of the business located in that country. The allocation would be based on a formula that would reflect the economic factors that contribute to profit making. While the GPS draws on elements of the traditional formulary apportionment and profit split methods, it also differs from them. The author discusses in detail the key issues involved in designing the GPS. She also presents and evaluates the main policy and pragmatic justifications for the adoption of this innovative approach. The author argues that the GPS is not only theoretically and practically superior to traditional income allocation methods, but also consistent with the arm’s-length principle. On the basis of historical developments, interpretation of article 9 of the OECD model tax convention, and international tax policy considerations, the author establishes that the GPS is not a radical departure from the arm’s-length principle, but rather a natural development in its evolution. She concludes that the law of evolution ison the side of reform because the GPS would provide for a fair and effective allocation of income derived from globally integrated business activities.

Evolutionary approach to revising modernization theory : an introduction to the credible polity

The future global distribution of the political regimes of countries, just like that of their economic incomes, displays a surprising tendency for polarization into only two clubs of convergence at the extrema. This, in itself, is a persuasive reason to analyze afresh the logical validity of an endogenous theory for political and economic development inherent in modernization theory. I suggest how adopting a simple evolutionary game theoretic view on the subject allows an explanation for these parallel clubs of convergence in political regimes and economic income within the framework of existing research in democratization theory. I also suggest how instrumental action can be methodically introduced into such a setup using learning strategies adopted by political actors. These strategies, based on the first principles of political competition, are motivated by introducing the theoretical concept of a Credible Polity.

A GA-PSO layered encoding evolutionary approach to 0/1 knapsack optimization

This paper presents a layered encoding cascade evolutionary approach to solve a 0/1 knapsack optimization problem. A layered encoding structure is proposed and developed based on the schema theorem and the concepts of cascade correlation and multi-population evolutionary algorithms. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined with the proposed layered encoding structure to form a generic optimization model denoted as LGAPSO. In order to enhance the finding of both local and global optimum in the evolutionary search, the model adopts hill climbing evaluation criteria, feature of strength Pareto evolutionary approach (SPEA) as well as nondominated spread lengthen criteria. Four different sizes benchmark knapsack problems are studied using the proposed LGAPSO model. The performance of LGAPSO is compared to that of the ordinary multi-objective optimizers such as VEGA, NSGA, NPGA and SPEA. The proposed LGAPSO model is shown to be efficient in improving the search of knapsack’s optimum, capable of gaining better Pareto trade-off front.

Popularity of reinforcement-based and belief-based learning models: an evolutionary approach

In an evolutionary model, players from a given population meet randomly in pairs each instant to play a coordination game. At each instant, the learning model used is determined via some replicator dynamics that respects payoff fitness. We allow for two such models: a belief-based best-response model that uses a costly predictor, and a costless reinforcement-based one. This generates dynamics over the choice of learning models and the consequent choices of endogenous variables. We report conditions under which the long run outcomes are efficient (or inefficient) and they support the exclusive use of either of the models (or their co-existence).

Packaging controlled experiments using an evolutionary approach based on ontology

A body of knowledge in Software Engineering requires experiments replications. The knowledge generated by a study is registered in the so-called lab package, which, must be reviewed by an eventual research group with the intention to replicate it. However, researchers face difficulties reviewing the lab package, what leads to problems in share knowledge among research groups. Besides that, the lack of standardization is an obstacle to the integration of the knowledge from an isolated study in a common body of knowledge. In this sense, ontologies can be applied, since they can be seen as a standard that promotes the shared understanding of the experiment information structure. In this paper, we present a workflow to generate lab packages based on EXPEiiQntology, an ontology of controlled experiments domain. In addition, by means of lab packages instantiation, it is possible to evolve the ontology, in order to deal with new concepts that may appear in different lab packages. The iterative ontology evolution aims at achieve a standard that is able to accommodate different lab packages and, hence, facilitate to review and understand their content.