Financial markets, ageing and social welfare

This paper considers some of the economic impacts that demographic change may have in developed economies over the next fifty years. I focus on the role that financial markets might play in economies where the pressure on government-run unfunded pension systems is likely to rise. The role of unfunded schemes is considered in a world where financial markets are incomplete and important types of risk cannot easily be offset by trading. How demographic shifts might affect labour productivity, asset prices and aggregate output is investigated using a simulation model of an economy where population structure is changing.

Risk, Return and Portfolio Allocation under Alternative Pension Arrangements with Imperfect Financial Markets

This paper uses stochastic simulations on calibrated models to assess the steady state impact of different pension arrangements in an environment where financial markets are less than perfect. Surprisingly little is known about the optimal split between funded and unfunded systems when there are sources of uninsurable risk that are allocated in different ways by different types of pension system and where there are imperfections in financial markets (eg transactions costs or adverse selection) . This paper calculates the expected welfare of agents in different economies where in the steady state the importance of unfunded, state pensions differs. We estimate how the optimal level of unfunded, state pensions depends on rate of return and income risks and also upon the actuarial fairness of annuity contracts. We focus on the case of Japan where aging is rapid and unfunded pensions are currently generous.Pensions; portfolio allocation, demographics; annuities; risk-sharing

Temporal learning in the cerebellum: The microcircuit model

The cerebellum is that part of the brain which coordinates motor reflex behavior. To perform effectively, it must learn to generate specific motor commands at the proper times. We propose a fundamental circuit, called the MicroCircuit, which is the minimal ensemble of neurons both necessary and sufficient to learn timing. We describe how learning takes place in the MicroCircuit, which then explains the global behavior of the cerebellum as coordinated MicroCircuit behavior

Studies in the biosynthesis of triterpenoids

Imperial Users onl

Modelling the cAMP pathway using BioNessie, and the use of BVP techniques for solving ODEs (Poster Presentation)

Copyright @ 2007 Gu et al; licensee BioMed Central LtdBiochemists often conduct experiments in-vivo in order to explore observable behaviours and understand the dynamics of many intercellular and intracellular processes. However an intuitive understanding of their dynamics is hard to obtain because most pathways of interest involve components connected via interlocking loops. Formal methods for modelling and analysis of biochemical pathways are therefore indispensable. To this end, ODEs (ordinary differential equations) have been widely adopted as a method to model biochemical pathways because they have an unambiguous mathematical format and are amenable to rigorous quantitative analysis. BioNessie http://www.bionessie.com webcite is a workbench for the composition, simulation and analysis of biochemical networks which is being developed in by the Systems Biology team at the Bioinformatics Research Centre as a part of a large DTI funded project 'BPS: A Software Tool for the Simulation and Analysis of Biochemical Networks' http://www.brc.dcs.gla.ac.uk/projects/dti_beacon webcite. BioNessie is written in Java using NetBeans Platform libraries that makes it platform independent. The software employs specialised differential equations solvers for stiff and non-stiff systems to produce model simulation traces. BioNessie provides a user-friendly interfact that comes up with an intuitive tree-based graphical layout, an edition function to SBML-compatible models and feature of data output

The Impact of Children’s Connection to Nature: A Report for the Royal Society for the Protection of Birds (RSPB)

Connecting with nature should be part of every child’s life as it has the potential to aid nature’s revival while benefiting the child. To embed nature connection within our social norms, there is a need to be able to understand the benefits and set targets for levels of nature connection. This report presents findings on the impact of connection to nature from a survey of 775 children, using the child as the unit of analysis, rather than aggregated data. The results demonstrated that children who were more connected to nature had significantly higher English attainment, although this wasn’t repeated for Mathematics. Further, the 1.5 Connection to Nature Index (CNI) level was found to be a significant threshold across other measures, with those children with a CNI of 1.5 or above having significantly higher health, life satisfaction, pro-environmental behaviours and pro-nature behaviours. The analysis found strong correlations between CNI and pro-nature behaviours and pro-environmental behavior. A positive correlation was also evident between CNI and days spent outdoors and days spent in nature over the past week, suggesting that the more time spent in nature is associated with child’s connection to nature. Finally, weak correlations were found between connection to nature, health and life satisfaction. When more refined attainment results for English were explored, (n = 512) further weak correlations were found between English attainment and attendance, English and life satisfaction, and between English attainment and connection to nature. There are a multitude of factors associated with a child’s English attainment, so, although the correlations are weak, it is noteworthy that connection to nature is as important to children’s achievement in English as life satisfaction and attendance at school

Joy and calm: how an evolutionary functional model of affect regulation informs positive emotions in nature

Key theories of the human need for nature take an evolutionary perspective, and many of the mental well-being benefits of nature relate to positive affect. As affect has a physiological basis, it is important to consider these benefits alongside regulatory processes. However, research into nature and positive affect tends not to consider affect regulation and the neurophysiology of emotion. This brief systematic review and meta-analysis presents evidence to support the use of an existing evolutionary functional model of affect regulation (the three circle model of emotion) that provides a tripartite framework in which to consider the mental well-being benefits of nature and to guide nature-based well-being interventions. The model outlines drive, contentment and threat dimensions of affect regulation based on a review of the emotion regulation literature. The model has been used previously for understanding mental well-being, delivering successful mental health-care interventions and providing directions for future research. Finally, the three circle model is easily understood in the context of our everyday lives, providing an accessible physiological-based narrative to help explain the benefits of nature

Measuring time preferences

We review research that measures time preferences—i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.National Institutes of Health (NIA R01AG021650 and P01AG005842) and the Pershing Square Fund for Research in the Foundations of Human Behavior

Machine Learning for Adaptive Computer Game Opponents

This thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can be integrated into a modern computer game without have a detrimental effect on game performance, then experimenting with different machine learning techniques to maximize the computer player's performance. Experiments use three machine learning techniques; static prediction models, continuous learning, and reinforcement learning. Static models show the highest initial performance but are not able to beat a simple opponent. Continuous learning is able to improve the performance achieved with static models but the rate of improvement drops over time and the computer player is still unable to beat the opponent. Reinforcement learning methods have the highest rate of improvement but the lowest initial performance. This limits the effectiveness of reinforcement learning because a large number of episodes are required before performance becomes sufficient to match the opponent