What Do Complex Adaptive Systems Look Like and What Are the Implications for Innovation Policy?

This paper explores the use of complex adaptive systems theory in development policy analysis using a case study drawn from recent events in Uganda. It documents the changes that took place in the farming system in Soroti district during an outbreak of African cassava mosaic virus disease (ACMVD) and the subsequent decline in cassava production - the main staple food in the area. Resultant adaptation impacts are analysed across cropping, biological, economic and social systems each of which operate as an interlinked sub-system. The policy implications of this story suggest a policy agenda that recognises adaptation capacity as the life blood of complex adaptive systems. Since these types of systems are found in all realms of human activity, it follows that strengthening this capacity is a key developmental priority that requires linking together new configurations of actors and resources to tackle an ever-changing set of contexts.Complex Adaptive Systems, Innovation Policy, Uganda, Cassava, Adaptation Capacity, Smallholder Production, Policy

Thermodynamics of adiabatic feedback control

We study adaptive control of classical ergodic Hamiltonian systems, where the controlling parameter varies slowly in time and is influenced by system's state (feedback). An effective adiabatic description is obtained for slow variables of the system. A general limit on the feedback induced negative entropy production is uncovered. It relates the quickest negentropy production to fluctuations of the control Hamiltonian. The method deals efficiently with the entropy-information trade off.Comment: 6 pages, 1 figur

A Function-Behaviour-Structure design methodology for adaptive production systems

Adaptive production systems are a key trend in modern advanced manufacturing. This stems from the requirement for the system to respond to disruption , either in the form of product changes or changes to other operational parameters. The design and reconfig-uration of these systems is therefore a unique challenge for the community. One approach to systems design is based on functional and behavioural modelling, drawn from the field of design theory. Existing approaches suffer from lack of focus on the adaptive properties of the system. While traditional production systems design focusses on the physical system structure and associated processes, new approaches based on functional and behavioural models are particularly suited to addressing the challenges of disruptive production environments resulting from Industry 4.0 and similar trends. We therefore present a Function-Behaviour-Structure (FBS) methodology for Evolvable Assembly Systems (EAS), a class of self-adaptive reconfigurable production systems, comprising an ontology model and design process. The ontology model provides definitions for Function, Structure, and Behaviour of an adaptive production system. This model is used as the input to a functional modelling design process for EAS-like systems , where the design process must be integrated into the system control behaviour. The framework is illustrated with an example taken from a real EAS instan-tiation using industrial hardware

Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems