Replicators, lineages, and interactors

AbstractThe target article argues that whole groups can act as interactors in an evolutionary process. We believe that Smaldino's discussion would be advanced by a more thorough analysis of the appropriate replicators and lineages for this model. We show that cultural evolution is necessarily a separate process from cultural group selection, and we also illustrate that the two processes may influence each other as demonstrated by an agent-based model of communicating food-processing skills.</jats:p

Introduction. Modelling natural action selection

Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder. Action selection is the task of resolving conflicts between competing behavioural alternatives, or, more simply put, of deciding ‘what to do next’. As a general problem facing all autonomous beings—animals and artificial agents—it has exercised the minds of scientists from many disciplines: those concerned with understanding the biological bases of behaviour (ethology, neurobiology and psychology) and those concerned with building artefacts, real or simulated, that behave appropriately in complex worlds (artificial intelligence, artificial life and robotics). Work in these different domains has established a wide variety of methodologies that address the same underlying problems from different perspectives. One approach to characterizing this multiplicity of methods is to distinguish between the analytical and the synthetic branches of the behavioural and brain sciences (Braitenberg 1986). From the perspective of analytical science, an important goal is to describe transitions in behaviour; these can occur at many different temporal scales, and can be considered as instances of ‘behavioural switching’ or, more anthropomorphically, as ‘choice points’. Analytical approaches also seek to identify the biological substrates that give rise to such transitions, for instance, by probing in the nervous system to find critical components—candidate action-selection mechanisms—on which effective and appropriate switching may depend. Beyond such descriptions, of course, a central goal of behavioural science is to explain why any observed transition (or sequence of transitions) occurs in a given context, perhaps referencing such explanation to normative concepts such as ‘utility’ or ‘fitness’. These explanations may also make use of mechanistic accounts that explain how underlying neural control systems operate to generate observed behavioural outcomes. It is at the confluence of these mechanistic and normative approaches that the synthetic approach in science is coming to have an increasing influence. The experimentalist seeks the help of the mathematician or engineer and asks ‘what would it take to build a system that acts in this way?’ Modelling—the synthesis of artificial systems that mimic natural ones—has always played an important role in biology; however, the last few decades have seen a dramatic expansion in the range of modelling methodologies that have been employed. Formal, mathematical models with provable properties continue to be of great importance (e.g. Bogacz et al. 2007; Houston et al. 2007). Now, added to these, there is a burgeoning interest in larger-scale simulations that allow the investigation of systems for which formal mathematical solutions are, as a result of their complexity, either intractable or simply unknown. However, synthetic models, once built, may often be elucidated by analytical techniques; thus synthetic and analytical approaches are best pursued jointly. Analysis of a formally intractable simulation often consists of observing the system's behaviour then measuring and describing it using many of the same tools as traditional experimental science (Bryson et al. 2007). Such an analysis can serve to uncover heuristics for the interpretation of empirical data as well as to generate novel hypotheses to be tested experimentally. The questions to be addressed in considering models of action selection include: is the model sufficiently constrained by biological data that its functioning can capture interesting properties of the natural system of interest? Do manipulations of the model, intended to mirror scientific procedures or observed natural processes, result in similar outcomes to those seen in real life? Does the model make predictions? Is the model more complex than it needs to be in order to describe a phenomenon, or is it too simple to engage with empirical data? A potential pitfall of more detailed computational models is that they may trade the sophistication with which they match biological detail with comprehensibility. The scientist is then left with two systems, one natural and the other synthesized, neither of which is well understood. Hence, the best models hit upon a good trade-off between accurately mimicking key properties of a target biological system at the same time as remaining understandable to the extent that new insights into the natural world are generated. In this theme issue, we present a selection of some of the most promising contemporary approaches to modelling action selection in natural systems. The range of methodologies is broad—from formal mathematical models, through to models of artificial animals, here called agents, embedded in simulated worlds (often containing other agents). We also consider mechanistic accounts of the neural processes underlying action selection through a variety of computational neuroscience and connectionist approaches. In this article, we summarize the main substantive areas of this theme issue and the contributions of each article and then return briefly to a discussion of the modelling techniques

The distribution of transit durations for Kepler planet candidates and implications for their orbital eccentricities

‘In these times, during the rise in the popularity of institutional repositories, the Society does not forbid authors from depositing their work in such repositories. However, the AAS regards the deposit of scholarly work in such repositories to be a decision of the individual scholar, as long as the individual's actions respect the diligence of the journals and their reviewers.’ Original article can be found at : http://iopscience.iop.org/ Copyright American Astronomical SocietyDoppler planet searches have discovered that giant planets follow orbits with a wide range of orbital eccentricities, revolutionizing theories of planet formation. The discovery of hundreds of exoplanet candidates by NASA's Kepler mission enables astronomers to characterize the eccentricity distribution of small exoplanets. Measuring the eccentricity of individual planets is only practical in favorable cases that are amenable to complementary techniques (e.g., radial velocities, transit timing variations, occultation photometry). Yet even in the absence of individual eccentricities, it is possible to study the distribution of eccentricities based on the distribution of transit durations (relative to the maximum transit duration for a circular orbit). We analyze the transit duration distribution of Kepler planet candidates. We find that for host stars with T > 5100 K we cannot invert this to infer the eccentricity distribution at this time due to uncertainties and possible systematics in the host star densities. With this limitation in mind, we compare the observed transit duration distribution with models to rule out extreme distributions. If we assume a Rayleigh eccentricity distribution for Kepler planet candidates, then we find best fits with a mean eccentricity of 0.1-0.25 for host stars with T ≤ 5100 K. We compare the transit duration distribution for different subsets of Kepler planet candidates and discuss tentative trends with planetary radius and multiplicity. High-precision spectroscopic follow-up observations for a large sample of host stars will be required to confirm which trends are real and which are the results of systematic errors in stellar radii. Finally, we identify planet candidates that must be eccentric or have a significantly underestimated stellar radius.Peer reviewedFinal Accepted Versio

Kepler-22b: A 2.4 Earth-radius Planet in the Habitable Zone of a Sun-like Star

A search of the time-series photometry from NASA's Kepler spacecraft reveals a transiting planet candidate orbiting the 11th magnitude G5 dwarf KIC 10593626 with a period of 290 days. The characteristics of the host star are well constrained by high-resolution spectroscopy combined with an asteroseismic analysis of the Kepler photometry, leading to an estimated mass and radius of 0.970 +/- 0.060 MSun and 0.979 +/- 0.020 RSun. The depth of 492 +/- 10ppm for the three observed transits yields a radius of 2.38 +/- 0.13 REarth for the planet. The system passes a battery of tests for false positives, including reconnaissance spectroscopy, high-resolution imaging, and centroid motion. A full BLENDER analysis provides further validation of the planet interpretation by showing that contamination of the target by an eclipsing system would rarely mimic the observed shape of the transits. The final validation of the planet is provided by 16 radial velocities obtained with HIRES on Keck 1 over a one year span. Although the velocities do not lead to a reliable orbit and mass determination, they are able to constrain the mass to a 3{\sigma} upper limit of 124 MEarth, safely in the regime of planetary masses, thus earning the designation Kepler-22b. The radiative equilibrium temperature is 262K for a planet in Kepler-22b's orbit. Although there is no evidence that Kepler-22b is a rocky planet, it is the first confirmed planet with a measured radius to orbit in the Habitable Zone of any star other than the Sun.Comment: Accepted to Ap

Implications of serial measurements of natriuretic peptides in heart failure: insights from BIOSTAT‐CHF

No abstract available

The Behavior Oriented Design of an Unreal Tournament Character

Abstract. This paper presents a case study for using a relatively recently developed methodology, Behavior Oriented Design, to develop an Intelligent Virtual Agent (IVA). Our usability study was conducted in Unreal Tournament using the game Capture The Flag. The final agent displays reasonably competent behavior: she is able to pursue multiple goals simultaneously and produce well-ordered behavior.

Where Should Complexity Go? Cooperation in Complex Agents with Minimal Communication

The `Radical Agent Concept&apos; in this chapter is that communication between agents in a MAS should be the simplest part of the system. When extensive real-time coordination between modules is required, then those modules should probably be considered elements of a single modular agent rather than as agents themselves. The advantage of this distinction is that system developers can then leverage standard software-engineering practices and more centralized coordination mechanisms to reduce the over-all complexity of the system. In this chapter I provide arguments for this point and also examples, both from nature and from my own research in building modular agents