Comparative Approach between Organizational Life Cycle and Rational Biological Model

This paper proposes an analogy between rational biological model and the organizations’ development during their existence. So, organizations’ "birth" or creation are considered the result of genetic algorithms, transformations are identified with changes that aim the adapting to the environment, and finally the ”death” treats the state of crisis and bankruptcy. In every stage of life there are proposals to increase it, by extension of states identified in the human area and not taken into account in the artificial one, which must learn from the first system, which we consider superior in the process of evolution. Although the authors’ approaches, do not build operational models to support the thesis presented, ask questions and focus on elements of management philosophy that "tomorrow" will certainly be resolved.genetic algorithms; organizational pathology; step by step evolution; tandem management change - organizational change.

Cultural Learning in a Dynamic Environment: an Analysis of Both Fitness and Diversity in Populations of Neural Network Agents

Evolutionary learning is a learning model that can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher/pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. This paper examines the effects of cultural learning on the evolutionary process of a population of neural networks. In particular, the paper examines the genotypic and phenotypic diversity of a population as well as its fitness. Using these measurements, it is possible to examine the effects of cultural learning on the population's genetic makeup. Furthermore, the paper examines whether cultural learning provides a more robust learning mechanism in the face of environmental changes. Three benchmark tasks have been chosen as the evolutionary task for the population: the bit-parity problem, the game of tic-tac-toe and the game of connect-four. Experiments are conducted with populations employing evolutionary learning alone and populations combining evolutionary and cultural learning in an environment that changes dramatically.Cultural Learning, Dynamic Environments, Diversity, Multi-Agent Systems, Artificial Life

On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers