Scientists' coping strategies in an evolving research system: the case of life scientists in the UK

Scientists in academia have struggled to adjust to a policy climate of uncertain funding and loss of freedom from direction and control. How UK life scientists have negotiated this challenge, and with what consequences for their research and the research system, is the empirical entrance point of this paper. We find that policy impacts can be modulated and buffered by strategies and compromises devised and deployed at research performer level. This shifts conceptualisation from terms of responses to one of more or less proactive strategies of scientists and science organisations which add up, intentionally or unintentionally, to shifts in the overall system

Empowerment and State-dependent Noise : An Intrinsic Motivation for Avoiding Unpredictable Agents

Empowerment is a recently introduced intrinsic motivation algorithm based on the embodiment of an agent and the dynamics of the world the agent is situated in. Computed as the channel capacity from an agent’s actuators to an agent’s sensors, it offers a quantitative measure of how much an agent is in control of the world it can perceive. In this paper, we expand the approximation of empowerment as a Gaussian linear channel to compute empowerment based on the covariance matrix between actuators and sensors, incorporating state dependent noise. This allows for the first time the study of continuous systems with several agents. We found that if the behaviour of another agent cannot be predicted accurately, then interacting with that agent will decrease the empowerment of the original agent. This leads to behaviour realizing collision avoidance with other agents, purely from maximising an agent’s empowermentFinal Accepted Versio

Evolving strategies for single-celled organisms in multi-nutrient environments

When micro-organisms are in environments with multiple nutrients, they often preferentially utilise one first. A second is only utilised once the first is exhausted. Such a two-phase growth pattern is known as diauxic growth. Experimentally, this manifests itself through two distinct exponential growth phases separated by a lag phase of arrested growth. The dura- tion of the lag phase can be quite substantial. From an evolu- tionary point of view the existence of a lag phase is somewhat puzzling because it implies a substantial loss of growth op- portunity. Mutants with shorter lag phases would be prone to outcompete those with longer phases. Yet in nature, diauxic growth with lag phases appears to be a robust phenomenon. We introduce a model of the evolution of diauxic growth that captures the basic interactions regulating it in bacteria. We observe its evolution without a lag phase. We conclude that the lag phase is an adaptation that is only beneficial when fit- ness is averaged over a large number of environments

Complex Agent Networks explaining the HIV epidemic among homosexual men in Amsterdam

Simulating the evolution of the Human Immunodeficiency Virus (HIV) epidemic requires a detailed description of the population network, especially for small populations in which individuals can be represented in detail and accuracy. In this paper, we introduce the concept of a Complex Agent Network(CAN) to model the HIV epidemics by combining agent-based modelling and complex networks, in which agents represent individuals that have sexual interactions. The applicability of CANs is demonstrated by constructing and executing a detailed HIV epidemic model for men who have sex with men (MSM) in Amsterdam, including a distinction between steady and casual relationships. We focus on MSM contacts because they play an important role in HIV epidemics and have been tracked in Amsterdam for a long time. Our experiments show good correspondence between the historical data of the Amsterdam cohort and the simulation results.Comment: 21 pages, 4 figures, Mathematics and Computers in Simulation, added reference

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

Characterising Livestock System Zoonoses Hotspots

A systematic review of the published literature was undertaken, to explore the ability of different types of model to help identify the relative importance of different drivers leading to the development of zoonoses hotspots. We estimated that out of 373 papers we included in our review, 108 papers touched upon the objective of 'Assessment of interventions and intervention policies', 75 addressed the objective of 'Analysis of economic aspects of disease outbreaks and interventions', 67 the objective of 'Prediction of future outbreaks', but only 37 broadly addressed the objective of 'Sensitivity analysis to identify criteria leading to enhanced risk'. Most models of zoonotic diseases are currently capturing outbreaks over relatively short time and largely ignoring socio-economic drivers leading to pathogen emergence, spill-over and spread. In order to study long-term changes we need to understand how socio-economic and climatic changes affect structure of livestock production and how these in turn affect disease emergence and spread. Models capable of describing this processes do not appear to exist, although some progress has been made in linking social and economical aspects of livestock production and in linking economics to disease dynamics. Henceforth we conclude that a new modelling framework is required that expands and formalises the 'one world, one health' strategy, enabling its deployment in the re-thinking of prevention and control strategies. Although modelling can only provide means to identify risks associated with socio-economic changes, it can never be a substitute for data collection. Finally, we note that uncertainty analysis and uncertainty communication form a key element of modelling process and yet are rarely addressed