This paper contributes to the (re-)conceptualisation of responsible innovation by proposing an evolutionary economic approach that focuses on the role of consumers in the innovation process. After a discussion of the philosophical foundations and ethical implications of this approach, which bears an explanatory potential that has not been adequately considered in previous discussions of responsible innovation, we present a first step towards capturing the important but often neglected role of consumers in innovation processes (including responsible innovation): We propose an agent-based (...) model that incorporates a multidimensional space of characteristics in which new products or services are represented by more than the mere aspect of price and quality. Instead, innovations are denoted by a large set of characteristics, including also negative or harmful ones. The model is used to illustrate that consumers’ heterogeneity and bounded rationality – even if considered in a simple manner – indeed play a crucial role in the creation and diffusion of responsible innovation which can and should be used for further work in this field and for possible extensions of the model. (shrink)

To perceive an affordance is to perceive an object or situation as presenting an opportunity for action. The concept of affordances has been taken up across wide range of disciplines, including AI. I explore an interesting extension of the concept of affordances in robotics. Among the affordances that artificial systems have been engineered to detect are affordances to deliberate. In psychology, affordances are typically limited to bodily action, so the it is noteworthy that AI researchers have found it helpful to (...) extend the concept to encompass mental actions. I propose that psychologists can learn from this extension, and argue that human subjects can perceive mental affordances, such as affordances to attend, affordances to imagine and affordances to count. (shrink)

Different disciplines have analyzed binary choices to model collective behavior in human systems. Several situations in which social dilemma arise can be modeled as N-person prisoner’s dilemma games including homeland security, public goods, international political economy among others. The purpose of this study is to develop an analytical solution to the N-person prisoner’s dilemma game when boundedly rational agents interact in a population. Previous studies in the literature consider the case in which cooperators and defectors have the same learning factors. (...) We obtain an analytical equation to find equilibria in the N-person prisoner’s dilemma game in the general case when agents have different learning factors. We also introduce a more realistic approach where probability values are bounded between zero and one and therefore eliminates the possibility of infeasible probability values. Since no analytic solution can be derived in this case, agent based simulation is used to analyze the asymptotic behavior of the resulted dynamical system. (shrink)

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...) purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding. (shrink)

This research calls for attention to complexity theory and the integration of complexity methodologies in policy research. A complexity approach in research practice requires a systems worldview and recognition of non-linearity, networks, self-organization, emergence, and feedback in policy. Simply, if a phenomenon is complex and can be explored from varied contexts and scales, the conceptual frame, and the methodical approach should be able to address the complexity. Complexity science has the capacity to account for complexity in varied contexts, on varied (...) scales, and with an interrelated and interdependent environment. Further, if policy systems are at times simple, complicated, complex, and chaotic, then complexity mythologies combined with traditional qualitative and quantitative methods can bring to light the workings of the elusive black box. Complexity allows for triangulation of both linear and non-linear methodologies integrating qualitative, quantitative, network analysis, and agent-based models, as well as other methodologies. Complexity science is not the panacea to solve all policy problems with perfection. But if you do not acknowledge the complexity you will be missing out on the opportunity to shape patterns of sustainability, to effectively manage complex policy problems to someday solve them. (shrink)

Humans’ interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model (...) system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB’s evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights. (shrink)