Adaptive testing of a deterministic implementation against a nondeterministic finite state machine

A number of authors have looked at the problem of deriving a checking experiment from a nondeterministic finite state machine that models the required behaviour of a system. We show that these methods can be extended if it is known that the implementation is equivalent to some (unknown) deterministic finite state machine. When testing a deterministic implementation, the test output provides information about the implementation under test and can thus guide future testing. The use of an adaptive test process is thus proposed

Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity

We introduce a model of the fidelity of open systems - fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system's notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system's steady state and the second one capturing that system's dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect's qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model's aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems" (ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile

Towards engineering ontologies for cognitive profiling of agents on the semantic web

Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

Stochastic oscillations of adaptive networks: application to epidemic modelling

Adaptive-network models are typically studied using deterministic differential equations which approximately describe their dynamics. In simulations, however, the discrete nature of the network gives rise to intrinsic noise which can radically alter the system's behaviour. In this article we develop a method to predict the effects of stochasticity in adaptive networks by making use of a pair-based proxy model. The technique is developed in the context of an epidemiological model of a disease spreading over an adaptive network of infectious contact. Our analysis reveals that in this model the structure of the network exhibits stochastic oscillations in response to fluctuations in the disease dynamic.Comment: 11 pages, 4 figure

A computational theory of willingness to exchange, ESRI working paper no. 477, January 2014

A new model of exchange is presented following Marr’s conception of a “computational theory”. The model combines assumptions from perceptual theory and economic theory to develop a highly generalised formal model. The approach departs from previous models by focussing not on how ownership alters preferences, but instead on difficulties inherent in the process of exchange in real markets. Agents treat their own perceptual uncertainty when valuing a potential exchange item as a signal regarding the variability of potential bids and offers. The analysis shows how optimising agents, with no aversion to risk or loss, will produce an endowment effect of variable degree, in line with empirical findings. The model implies that the endowment effect is not a laboratory finding that may not occur in real markets, but rather a market phenomenon that may not occur in the laboratory

Neo-Keynesian and Neo-Classical Macroeconomic Models: Stability and Lyapunov Exponents

The non-linear approach to economic dynamics enables us to study traditional economic models using modified formulations and different methods of solution. In this article we compare dynamical properties of Keynesian and Classical macroeconomic models. We start with an extended dynamical IS-LM neoclassical model generating behaviour of the real product, interest rate, expected inflation and the price level over time. Limiting behaviour, stability, and existence of limit cycles and other specific features of these models will be compared.macroeconomic models; Keynesian and classical model; nonlinear differential equations; linearization; asymptotical stability; Lyapunov exponents S