1,721,193 research outputs found
Jumping to bold conclusions: A book review of Amotz and Avishag Zahavi's "The Handicap Principle: A Missing Piece of Darwin's Puzzle"
No full textLevins and the lure of artificial worlds
No full textWhat is it about simulation models that has led some practitioners to treat them as potential sources of empirical data on the real-world systems being simulated; that is, to treat simulations as ‘artificial worlds’ within which to perform computational ‘experiments’? Here we use the work of Richard Levins as a starting point in identifying the appeal of this model building strategy, and proceed to account for why this appeal is strongest for computational modellers. This analysis suggests a perspective on simulation modelling that makes room for ‘artificial worlds’ as legitimate science without having to accept that they should be treated as sources of empirical dat
Exploring adaptation with evolutionary activity plots
No full textEvolutionary activity statistics and their visualization are introduced, and their motivation is explained. Examples of their use are described, and their strengths and limitations are discussed. References to more extensive or general accounts of these techniques are provided
Prospects for large-scale financial systems simulation
No full textAs the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research
Do roboticists dream of intelligent sheep? A book review of David McFarland's "Guilty Robots, Happy Dogs: The Question of Alien Minds"
No full textIt's a decent bet that right now there are more guilty "robots" roaming the internet on the lookout for your unguarded e-mail address than there will ever be real robot canines patrolling our homes and gardens. But this book is not primarily driven by the actualities of current or future robots, being more closely aligned with modern science fiction's take on robots as philosophical devices. Just as a confused amnesiac in an art-house movie is a perfect vehicle for extended meditations on the nature of identity, imagined moody androids provide seductive raw material for a good muse on our origins, purpose and morality. Dutifully, David McFarland opens and closes his new book with the imagined moral panic surrounding a humanoid traffic cop. Could one ever really be capable of replacing a person? Could one ever really be culpable, in place of its human designer, if it were to make some fatal error? The scenario is a brief distraction, though, because the book's central concern is not people but animals and the robots that might resemble them: think mechanical sniffer dogs, robot pack mules, carrier cyber-pigeons and maybe K9. After refocusing on this menagerie, McFarland sets sail for the deep waters surrounding an old question: what would it take for such a machine or animal to have a mind, one that would presumably be alien to our own? By approaching the problem from a bio-robotic direction, his hope is to navigate a route that avoids some of the choppier confusions. McFarland built a career as an Oxbridge roboticist and biologist, interpreting animals as if they were machines and machines as if they were animals. At times, it seems that he is maintaining the distinction only as a courtesy to the reader, having long since convinced himself that you might as well lump them together and proceed accordingly. He's happier equipping a robot guard dog with skunk-inspired stink-squirters than Taser guns, but it is this readiness to reach for an example from the world of animals rather than people that keeps the book on course. Careful use of research on crafty Caledonian crows, doggy dreams, self-sufficient slug-bots and vomiting pigeons allows him to steer clear of questions of (human) conscious experience until the later chapters. McFarland is on home territory dishing up a patented blend of behaviourism (infamously discredited) and economics (infamously dismal). By salvaging a surprisingly defensible hybrid of the two, he is able to use cost-benefit thinking to explain the critical balance of decision-making that a successful autonomous robot or animal must be capable of in order to continually "do the right thing". But before squaring up to McFarland's main event, the book has first to take in a daunting litany of philosophical positions, and while he trawls through them diligently, you get the feeling there is little joy in clearing the ground. Rather, he's fishing around in the science and philosophy of rationality and subjectivity (and tossing most of his catch straight back) in order to demonstrate that what prevents us from readily acknowledging the potential for fully fledged robot minds is just an "alienist" chauvinism that will dissolve as we come to regard robots (and some animals) as "us" rather than "them", despite their "alien lifestyles". This abrupt sociological turn is delayed until the final sentences, leaving the reader to reflect unaccompanied on just how alien a "lifestyle" would need to be before we begin to feel that there might not actually be "something that it is like" to be that alien something or someone, and they begin to feel the same about us
Co-evolutionary design: Implications for evolutionary robotics
No full textGenetic Algorithms (GAs) typically work on static fitness landscapes. In contrast, natural evolution works on fitness landscapes that change over evolutionary time as a result of (amongst other things) co-evolution. The attractions of co-evolutionary design techniques are discussed, and attempts to utilise co-evolution in the use of GAs as design tools are reviewed, before the implications of natural predator-prey co-evolution are considered. Utilising strict definitions of true and diffuse co-evolution provided by Janzen (1980), a distinction is drawn between two styles of evolutionary niche, Predator and Parasite. The former niche is robust with respect to environmental change and features systems that have had to solve evolutionary problems in ways that reveal general purpose design principles, whilst the nature of the latter is such that, despite being fragile and unsatisfactory in these respects, it is nevertheless evolutionarily successful. It is contested that if co-evolutionary design is to provide systems that solve problems in ways that reveal general purpose design principles, i.e. to provide robust styles of solution, true co-evolution must be abandoned in favour of diffuse co-evolutionary design regimes
The child in time: A book review of Denise Cummins and Colin Allen's "The Evolution of Mind"
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