Towards a Theory and Policy of Eco-Innovation - Neoclassical and (Co-)Evolutionary Perspectives

Innovation processes toward sustainable development (eco-innovations) have received increasing attention during the past years. Since existing theoretical and methodological frameworks do not address these problems adequately, research need can be identified to improve our understanding of innovation processes toward sustainability in their different dimensions, complex feedback mechanisms and interrelations. This paper discusses the potential contribution of neoclassical and (co-)evolutionary approaches from environmental and innovation economics to fill this gap. It is argued that both approaches have their merits and limits concerning a theory and policy of ecoinnovation. Neoclassical methods are most elaborated to analyze the efficiency of incentive systems which seems to be essential for stimulating innovation. Evolutionary approaches are more appropriate for analyzing long-term technological regime shifts. On this theoretical basis, a crucial question is if innovations toward sustainability can be treated like normal innovations or if a specific theory and policy are needed. Three specialties of eco-innovation are identified: the double externality problem, the regulatory push/pull effect and the increasing importance of social and institutional innovation. While the first two of them are widely ignored in innovation economics, the third is at least not elaborated appropriately. The consideration of these specialties may help to overcome market failure by establishing a specific eco-innovation policy and to avoid a "technology bias" by a broader understanding of innovation. Eco-innovation policy requires close coordination with environmental policy in all innovation phases. Environmental and eco-innovation policy can be regarded as complementarily. However, an environmental policy neglecting the potentially beneficial effects of a specific eco-innovation policy (especially in the invention phase) may lead to excessive economic costs. Due to the specialties of eco-innovation, it seems moreover to be crucial to strengthen the importance of social and institutional innovation in both eco-innovation theory and policy. --eco-innovation,innovation theory,co-evolution,double externality,regulatory push/pull effect,social innovation,institutional innovation

Evolution of swarming behavior is shaped by how predators attack

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of ``domains of danger.'' Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.Comment: 25 pages, 11 figures, 5 tables, including 2 Supplementary Figures. Version to appear in "Artificial Life

Evolution of Swarm Robotics Systems with Novelty Search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final publication will be available at link.springer.co

Exploring the remuneration ‘black box’: establishing an organizational learning insight into changing remuneration committee ‘social worlds’

Current executive compensation research posits a need to extend analysis beyond principalagent theory in order to explore the complex social influences and processes implicated in Remuneration Committee (RemCo) decision-making (e.g. Bender, 2007; Kakabadse et al, 2006; Main et al., 2007), particularly given the current uproar surrounding reported levels and structuring of executive remuneration. We respond to this international need by highlighting how innovative organizational learning theorizing can be integrated into further investigations of the remuneration ‘Black Box’, in order to focus attention upon the nuances of what and how organizational learning takes place in the remuneration process. Additionally, we note the importance of investigating the main actors and particularly their performance of complex roles within their rapidly evolving ‘social worlds’. By exploring the organizational learning phenomena implicated in executive remuneration, we argue that practitioners, regulatory bodies etc. can appreciate further the implications of their respective decision-making

A generic model of dyadic social relationships

We introduce a model of dyadic social interactions and establish its correspondence with relational models theory (RMT), a theory of human social relationships. RMT posits four elementary models of relationships governing human interactions, singly or in combination: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing. To these are added the limiting cases of asocial and null interactions, whereby people do not coordinate with reference to any shared principle. Our model is rooted in the observation that each individual in a dyadic interaction can do either the same thing as the other individual, a different thing or nothing at all. To represent these three possibilities, we consider two individuals that can each act in one out of three ways toward the other: perform a social action X or Y, or alternatively do nothing. We demonstrate that the relationships generated by this model aggregate into six exhaustive and disjoint categories. We propose that four of these categories match the four relational models, while the remaining two correspond to the asocial and null interactions defined in RMT. We generalize our results to the presence of N social actions. We infer that the four relational models form an exhaustive set of all possible dyadic relationships based on social coordination. Hence, we contribute to RMT by offering an answer to the question of why there could exist just four relational models. In addition, we discuss how to use our representation to analyze data sets of dyadic social interactions, and how social actions may be valued and matched by the agents

Processes, Roles and Their Interactions

Taking an interaction network oriented perspective in informatics raises the challenge to describe deterministic finite systems which take part in networks of nondeterministic interactions. The traditional approach to describe processes as stepwise executable activities which are not based on the ordinarily nondeterministic interaction shows strong centralization tendencies. As suggested in this article, viewing processes and their interactions as complementary can circumvent these centralization tendencies. The description of both, processes and their interactions is based on the same building blocks, namely finite input output automata (or transducers). Processes are viewed as finite systems that take part in multiple, ordinarily nondeterministic interactions. The interactions between processes are described as protocols. The effects of communication between processes as well as the necessary coordination of different interactions within a processes are both based on the restriction of the transition relation of product automata. The channel based outer coupling represents the causal relation between the output and the input of different systems. The coordination condition based inner coupling represents the causal relation between the input and output of a single system. All steps are illustrated with the example of a network of resource administration processes which is supposed to provide requesting user processes exclusive access to a single resource.Comment: In Proceedings IWIGP 2012, arXiv:1202.422