746 research outputs found
Theory of agent-based market models with controlled levels of greed and anxiety
We use generating functional analysis to study minority-game type market
models with generalized strategy valuation updates that control the psychology
of agents' actions. The agents' choice between trend following and contrarian
trading, and their vigor in each, depends on the overall state of the market.
Even in `fake history' models, the theory now involves an effective overall bid
process (coupled to the effective agent process) which can exhibit profound
remanence effects and new phase transitions. For some models the bid process
can be solved directly, others require Maxwell-construction type
approximations.Comment: 30 pages, 10 figure
Dynamics of on-line Hebbian learning with structurally unrealizable restricted training sets
We present an exact solution for the dynamics of on-line Hebbian learning in
neural networks, with restricted and unrealizable training sets. In contrast to
other studies on learning with restricted training sets, unrealizability is
here caused by structural mismatch, rather than data noise: the teacher machine
is a perceptron with a reversed wedge-type transfer function, while the student
machine is a perceptron with a sigmoidal transfer function. We calculate the
glassy dynamics of the macroscopic performance measures, training error and
generalization error, and the (non-Gaussian) student field distribution. Our
results, which find excellent confirmation in numerical simulations, provide a
new benchmark test for general formalisms with which to study unrealizable
learning processes with restricted training sets.Comment: 7 pages including 3 figures, using IOP latex2e preprint class fil
The Cavity Approach to Parallel Dynamics of Ising Spins on a Graph
We use the cavity method to study parallel dynamics of disordered Ising
models on a graph. In particular, we derive a set of recursive equations in
single site probabilities of paths propagating along the edges of the graph.
These equations are analogous to the cavity equations for equilibrium models
and are exact on a tree. On graphs with exclusively directed edges we find an
exact expression for the stationary distribution of the spins. We present the
phase diagrams for an Ising model on an asymmetric Bethe lattice and for a
neural network with Hebbian interactions on an asymmetric scale-free graph. For
graphs with a nonzero fraction of symmetric edges the equations can be solved
for a finite number of time steps. Theoretical predictions are confirmed by
simulation results. Using a heuristic method, the cavity equations are extended
to a set of equations that determine the marginals of the stationary
distribution of Ising models on graphs with a nonzero fraction of symmetric
edges. The results of this method are discussed and compared with simulations
Closure of Macroscopic Laws in Disordered Spin Systems: A Toy Model
We use a linear system of Langevin spins with disordered interactions as an
exactly solvable toy model to investigate a procedure, recently proposed by
Coolen and Sherrington, for closing the hierarchy of macroscopic order
parameter equations in disordered spin systems. The closure procedure, based on
the removal of microscopic memory effects, is shown to reproduce the correct
equations for short times and in equilibrium. For intermediate time-scales the
procedure does not lead to the exact equations, yet for homogeneous initial
conditions succeeds at capturing the main characteristics of the flow in the
order parameter plane. The procedure fails in terms of the long-term temporal
dependence of the order parameters. For low energy inhomogeneous initial
conditions and near criticality (where zero modes appear) deviations in
temporal behaviour are most apparent. For homogeneous initial conditions the
impact of microscopic memory effects on the evolution of macroscopic order
parameters in disordered spin systems appears to be mainly an overall slowing
down.Comment: 14 pages, LateX, OUTP-94-24
Statistical Mechanics of Dilute Batch Minority Games with Random External Information
We study the dynamics and statics of a dilute batch minority game with random
external information. We focus on the case in which the number of connections
per agent is infinite in the thermodynamic limit. The dynamical scenario of
ergodicity breaking in this model is different from the phase transition in the
standard minority game and is characterised by the onset of long-term memory at
finite integrated response. We demonstrate that finite memory appears at the
AT-line obtained from the corresponding replica calculation, and compare the
behaviour of the dilute model with the minority game with market impact
correction, which is known to exhibit similar features.Comment: 22 pages, 6 figures, text modified, references updated and added,
figure added, typos correcte
Feed-Forward Chains of Recurrent Attractor Neural Networks Near Saturation
We perform a stationary state replica analysis for a layered network of Ising
spin neurons, with recurrent Hebbian interactions within each layer, in
combination with strictly feed-forward Hebbian interactions between successive
layers. This model interpolates between the fully recurrent and symmetric
attractor network studied by Amit el al, and the strictly feed-forward
attractor network studied by Domany et al. Due to the absence of detailed
balance, it is as yet solvable only in the zero temperature limit. The built-in
competition between two qualitatively different modes of operation,
feed-forward (ergodic within layers) versus recurrent (non- ergodic within
layers), is found to induce interesting phase transitions.Comment: 14 pages LaTex with 4 postscript figures submitted to J. Phys.
Slowly evolving geometry in recurrent neural networks I: extreme dilution regime
We study extremely diluted spin models of neural networks in which the
connectivity evolves in time, although adiabatically slowly compared to the
neurons, according to stochastic equations which on average aim to reduce
frustration. The (fast) neurons and (slow) connectivity variables equilibrate
separately, but at different temperatures. Our model is exactly solvable in
equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e.
recall of one pattern). These show that, as the connectivity temperature is
lowered, the volume of the retrieval phase diverges and the fraction of
mis-aligned spins is reduced. Still one always retains a region in the
retrieval phase where recall states other than the one corresponding to the
`condensed' pattern are locally stable, so the associative memory character of
our model is preserved.Comment: 18 pages, 6 figure
Diagonalization of replicated transfer matrices for disordered Ising spin systems
We present an alternative procedure for solving the eigenvalue problem of
replicated transfer matrices describing disordered spin systems with (random)
1D nearest neighbor bonds and/or random fields, possibly in combination with
(random) long range bonds. Our method is based on transforming the original
eigenvalue problem for a matrix (where ) into an
eigenvalue problem for integral operators. We first develop our formalism for
the Ising chain with random bonds and fields, where we recover known results.
We then apply our methods to models of spins which interact simultaneously via
a one-dimensional ring and via more complex long-range connectivity structures,
e.g. dimensional neural networks and `small world' magnets.
Numerical simulations confirm our predictions satisfactorily.Comment: 24 pages, LaTex, IOP macro
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