137,858 research outputs found
Exploring NK Fitness Landscapes Using Imitative Learning
The idea that a group of cooperating agents can solve problems more
efficiently than when those agents work independently is hardly controversial,
despite our obliviousness of the conditions that make cooperation a successful
problem solving strategy. Here we investigate the performance of a group of
agents in locating the global maxima of NK fitness landscapes with varying
degrees of ruggedness. Cooperation is taken into account through imitative
learning and the broadcasting of messages informing on the fitness of each
agent. We find a trade-off between the group size and the frequency of
imitation: for rugged landscapes, too much imitation or too large a group yield
a performance poorer than that of independent agents. By decreasing the
diversity of the group, imitative learning may lead to duplication of work and
hence to a decrease of its effective size. However, when the parameters are set
to optimal values the cooperative group substantially outperforms the
independent agents
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems
In this work we introduce an evolutionary strategy to solve combinatorial
optimization tasks, i.e. problems characterized by a discrete search space. In
particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous
problem whose search space grows exponentially, increasing the number of
cities, up to becoming NP-hard. The solutions of the TSP can be codified by
arrays of cities, and can be evaluated by fitness, computed according to a cost
function (e.g. the length of a path). Our method is based on the evolution of
an agent population by means of an imitative mechanism, we define `partial
imitation'. In particular, agents receive a random solution and then,
interacting among themselves, may imitate the solutions of agents with a higher
fitness. Since the imitation mechanism is only partial, agents copy only one
entry (randomly chosen) of another array (i.e. solution). In doing so, the
population converges towards a shared solution, behaving like a spin system
undergoing a cooling process, i.e. driven towards an ordered phase. We
highlight that the adopted `partial imitation' mechanism allows the population
to generate solutions over time, before reaching the final equilibrium. Results
of numerical simulations show that our method is able to find, in a finite
time, both optimal and suboptimal solutions, depending on the size of the
considered search space.Comment: 18 pages, 6 figure
Stochastic simulation framework for the Limit Order Book using liquidity motivated agents
In this paper we develop a new form of agent-based model for limit order
books based on heterogeneous trading agents, whose motivations are liquidity
driven. These agents are abstractions of real market participants, expressed in
a stochastic model framework. We develop an efficient way to perform
statistical calibration of the model parameters on Level 2 limit order book
data from Chi-X, based on a combination of indirect inference and
multi-objective optimisation. We then demonstrate how such an agent-based
modelling framework can be of use in testing exchange regulations, as well as
informing brokerage decisions and other trading based scenarios
Imitative learning as a connector of collective brains
The notion that cooperation can aid a group of agents to solve problems more
efficiently than if those agents worked in isolation is prevalent, despite the
little quantitative groundwork to support it. Here we consider a primordial
form of cooperation -- imitative learning -- that allows an effective exchange
of information between agents, which are viewed as the processing units of a
social intelligence system or collective brain. In particular, we use
agent-based simulations to study the performance of a group of agents in
solving a cryptarithmetic problem. An agent can either perform local random
moves to explore the solution space of the problem or imitate a model agent --
the best performing agent in its influence network. There is a complex
trade-off between the number of agents N and the imitation probability p, and
for the optimal balance between these parameters we observe a thirtyfold
diminution in the computational cost to find the solution of the
cryptarithmetic problem as compared with the independent search. If those
parameters are chosen far from the optimal setting, however, then imitative
learning can impair greatly the performance of the group. The observed
maladaptation of imitative learning for large N offers an alternative
explanation for the group size of social animals
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