Individual and global adaptation in networks

The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of such networks over time. Such ‘adaptive networks’ underlie how gene-regulation networks evolve, how ecological networks self-organise, and how networks of strategic agents co-create social organisations. Although such domains are different in the details, they can each be characterised as networks of self-interested agents where agents alter network connections in the direction that increases their individual utility. Recent work shows that such dynamics are equivalent to associative learning, well-understood in the context of neural networks. Associative learning in neural substrates is the result of mandated learning rules (e.g. Hebbian learning), but in networks of autonomous agents ‘associative induction’ occurs as a result of local individual incentives to alter connections. Using results from a number of recent studies, here we review the theoretical principles that can be transferred between disciplines as a result of this isomorphism, and the implications for the organisation of genetic, social and ecological networks

Operation Modulus: Putting Christie into Practice in Gorbals

As part of its exploration of public service reform What Works Scotland carried out an evaluation of Operation Modulus, a highly successful, innovative, award winning violence and anti-social behaviour intervention targeted at a gang of young people in the Gorbals area of Glasgow, exploring why it was such a success. The aim of this case study was not to focus on how best to tackle issues related to young people and crime, but rather to show how the principles of public service reform as highlighted by the Christie Commission (2011) can best be operationalised. Operation Modulus is an exemplar of such reform, demonstrating what it means for public services in Scotland to put Christie into practice

Incremental construction of minimal acyclic finite-state automata

In this paper, we describe a new method for constructing minimal, deterministic, acyclic finite-state automata from a set of strings. Traditional methods consist of two phases: the first to construct a trie, the second one to minimize it. Our approach is to construct a minimal automaton in a single phase by adding new strings one by one and minimizing the resulting automaton on-the-fly. We present a general algorithm as well as a specialization that relies upon the lexicographical ordering of the input strings.Comment: 14 pages, 7 figure

Multidimensional Epistasis and the Advantage of Sex

Kondrashov and Kondrashov (2001) suggest that there is usually a disadvantage for sex in systems with multidimensional epistasis. They define systems of 'unidimensional epistasis' to be those where the fitness of a genotype is a function of the number of mutations it carries, and in contrast describe a system where the fitness of a genotype is a function of the numbers of mutations in two (or more) disjoint subsets of loci creating 'multidimensional epistasis'. In an example landscape an asexual population evolves fit genotypes about twice as fast as a sexual one. Here we examine other landscapes with multidimensional epistasis and find cases where an asexual population evolves fit genotypes 20 and 180 times slower than a sexual population