arXiv subjects
Publications and source records attributed to Neil F. Johnson.
Recent advances in nanofabrication and optical control have garnered tremendous interest in multi-qubit-cavity systems. Here we analyze a spin-glass version of such a nanostructure, solving analytically for the phase diagrams in both the matter and radiation subsystems in the replica symmetric regime. Interestingly, the resulting phase transitions turn out to be tunable simply by varying the matter-radiation coupling strength.
We discuss a method for predicting financial movements and finding pockets of predictability in the price-series, which is built around inferring the heterogeneity of trading strategies in a multi-agent trader population. This work explores extensions to our previous framework (arXiv:physics/0506134). Here we allow for more intelligent agents possessing a richer strategy set, and we no longer constrain the estimate for the heterogeneity of the agents to a probability space. We also introduce a scheme which allows the incorporation of models with a wide variety of agent types, and discuss a mechanism for the removal of bias from relevant parameters.
Evolving out-of-equilibrium networks have been under intense scrutiny recently. In many real-world settings the number of links added per new node is not constant but depends on the time at which the node is introduced in the system. This simple idea gives rise to the concept of accelerating networks, for which we review an existing definition and -- after finding it somewhat constrictive -- offer a new definition. The new definition provided here views network acceleration as a time dependent property of a given system, as opposed to being a property of the specific algorithm applied to grow the network. The defnition also covers both unweighted and weighted networks. As time-stamped network data becomes increasingly available, the proposed measures may be easily carried out on empirical datasets. As a simple case study we apply the concepts to study the evolution of three different instances of Wikipedia, namely, those in English, German, and Japanese, and find that the networks undergo different acceleration regimes in their evolution.
Information about quantum phase transitions in conventional condensed matter systems, must be sought by probing the matter system itself. By contrast, we show that mixed matter-light systems offer a distinct advantage in that the photon field carries clear signatures of the associated quantum critical phenomena. Having derived an accurate, size-consistent Hamiltonian for the photonic field in the well-known Dicke model, we predict striking behavior of the optical squeezing and photon statistics near the phase transition. The corresponding dynamics resemble those of a degenerate parametric amplifier. Our findings boost the motivation for exploring exotic quantum phase transition phenomena in atom-cavity, nanostructure-cavity, and nanostructure-photonic-band-gap systems.
We introduce a simple one-parameter network growth algorithm which is able to reproduce a wide variety of realistic network structures but without having to invoke any global information about node degrees such as preferential-attachment probabilities. Scale-free networks arise at the transition point between quasi-random and quasi-ordered networks. We provide a detailed formalism which accurately describes the entire network range, including this critical point. Our formalism is built around a statistical description of the inter-node linkages, as opposed to the single-node degrees, and can be applied to any real-world network -- in particular, those where node-node degree correlations might be important.
We analyze the dynamical response of the world's financial community to various types of unexpected events, including the 9/11 terrorist attacks as they unfolded on a minute-by-minute basis. We find that there are various 'species' of news, characterized by how quickly the news get absorbed, how much meaning and importance is assigned to it by the community, and what subsequent actions are then taken. For example, the response to the unfolding events of 9/11 shows a gradual collective understanding of what was happening, rather than an immediate realization. For news items which are not simple economic statements, and hence whose implications are not immediately obvious, we uncover periods of collective discovery during which collective opinions seem to oscillate in a remarkably synchronized way. In the case of a rumour, our findings also provide a concrete example of contagion in inter-connected communities. Practical applications of this work include the possibility of producing selective newsfeeds for specific communities, based on their likely impact.
We discuss the feasibility of predicting, managing and subsequently manipulating, the future evolution of a Complex Adaptive System. Our archetypal system mimics a population of adaptive, interacting objects, such as those arising in the domains of human health and biology (e.g. cells), financial markets (e.g. traders), and mechanical systems (e.g. robots). We show that short-term prediction yields corridors along which the model system will, with high probability, evolve. We show how the widths and average direction of these corridors varies in time as the system passes through regions, or pockets, of enhanced predictability and/or risk. We then show how small amounts of 'population engineering' can be undertaken in order to steer the system away from any undesired regimes which have been predicted. Despite the system's many degrees of freedom and inherent stochasticity, this dynamical, 'soft' control over future risk requires only minimal knowledge about the underlying composition of the constituent multi-agent population.
We report a remarkable universality in the patterns of violence arising in three high-profile ongoing wars, and in global terrorism. Our results suggest that these quite different conflict arenas currently feature a common type of enemy, i.e. the various insurgent forces are beginning to operate in a similar way regardless of their underlying ideologies, motivations and the terrain in which they operate. We provide a microscopic theory to explain our main observations. This theory treats the insurgent force as a generic, self-organizing system which is dynamically evolving through the continual coalescence and fragmentation of its constituent groups.
Recent advances in nanostructure fabrication and optical control, suggest that it will soon be possible to prepare collections of interacting two-level systems (i.e. qubits) within an optical cavity. Here we show theoretically that such systems could exhibit novel phase transition phenomena involving spin-glass phases. By contrast with traditional realizations using magnetic solids, these phase transition phenomena are associated with both matter and radiation subsystems. Moreover the various phase transitions should be tunable simply by varying the matter-radiation coupling strength.
We show that abrupt structural transitions can arise in functionally optimal networks, driven by small changes in the level of transport congestion. Our results offer an explanation as to why so many diverse species of network structure arise in Nature (e.g. fungal systems) under essentially the same environmental conditions. Our findings are based on an exactly solvable model system which mimics a variety of biological and social networks. We then extend our analysis by introducing a novel renormalization scheme involving cost motifs, to describe analytically the average shortest path across multiple-ring-and-hub networks. As a consequence, we uncover a 'skin effect' whereby the structure of the inner multi-ring core can cease to play any role in terms of determining the average shortest path across the network.
We analyze the effects of agents' decisions on the creation of congestion on a centralized network with ring-and-hub topology. We show that there are two classes of agents each displaying a distinct set of behaviours. The dynamics of the system are driven by an interplay between the formation of, and transition between, unique stable states that arise as the network is varied. We show how the flow of objects across the network can be understood in terms of the ordering and allocation of strategies. Our results show that the existence of congestion in a network is a dynamic process that is as much dependent on the agents' decisions as it is on the structure of the network itself.
We present a simple model for the formation of pairs in multi-agent populations of type A and B which move freely on a spatial network. Each agent of population A (and B) is labeled as Ai (and Bj) with i=1,.. NA (and j=1,..NB) and carries its own individual list of characteristics or 'phenotype'. When agents from opposite populations encounter one another on the network, they can form a relationship if not already engaged in one. The length of time for which any given pair stays together depends on the compatibility of the two constituent agents. Possible applications include the human dating scenario, and the commercial domain where two types of businesses A and B have members of each type looking for a business partner, i.e. Ai+Bj-->Rij. The pair Rij then survives for some finite time before dissociating Rij-->Ai+Bj. There are many possible generalizations of this basic setup. Here we content ourselves with some initial numerical results for the simplest of network topologies, together with some accompanying analytic analysis.
Multi-agent complex systems comprising populations of decision-making particles, have many potential applications across the biological, informational and social sciences. We show that the time-averaged dynamics in such systems bear a striking resemblance to conventional many-body physics. For the specific example of the Minority Game, this analogy enables us to obtain analytic expressions which are in excellent agreement with numerical simulations.
We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to saying that the current probability estimates of the strategy allocation across the multi-trader population are no longer accurate. Instead, new probability estimates need to be obtained by iterative updating, until a new 'pocket of predictability' emerges and reliable prediction can resume.
We present a renormalization scheme which simplifies the dynamics of an important class of interacting multi-qubit systems. We show that a wide class of M+1 qubit systems can be reduced to an equivalent n+1 qubit system with n equal to, or greater than, 2, for any M. Our renormalization scheme faithfully reproduces the overall dynamics of the original system including the entanglement properties. In addition to its direct application to atom-cavity and nanostructure systems, the formalism offers insight into a variety of situations ranging from decoherence due to a spin-bath with its own internal entanglement, through to energy transfer processes in organic systems such as biological photosynthetic units.
We show analytically that abrupt structural transitions can arise in functionally optimal networks, driven by small changes in the level of transport congestion. Our findings are based on an exactly solvable model system which mimics a variety of biological and social networks. Our results offer an explanation as to why such diverse sets of network structures arise in Nature (e.g. fungi) under essentially the same environmental conditions. As a by-product of this work, we introduce a novel renormalization scheme involving `cost motifs' which describes analytically the average shortest path across multiple-ring-and-hub networks.
The Minority Game (MG) is a basic multi-agent model representing a simplified and binary form of the bar attendance model of Arthur. The model has an informationally efficient phase in which the agents lack the capability of exploiting any information in the winning action time series. We develop a theory based on the ranking patterns of the strategies and the number of agents using a particular rank of strategies as the game proceeds. The theory is applied to calculate the distribution or probability density function in the number of agents making a particular decision. From the distribution, the standard deviation in the number of agents making a particular choice (e.g., the bar attendance) can be calculated in the efficient phase as a function of the parameter m specifying the agent's memory size. Since situations with tied cumulative performance of the strategies often occur in the efficient phase and they are critical in the decision making dynamics, the present theory is developed to take into account the effects of tied strategies. The analytic results are found to be in better agreement with numerical results, when compared with the simplest forms of the crowd-anticrowd theory in which cases of tied strategies are ignored. Hence the present theory represents a generalization of the crowd-anticrowd theory of multi-agent models, by focusing on the evolution of strategy ranking patterns instead of the performance of individual strategies. Paper to be presented in the 10th Annual Workshop on Economic Heterogeneous Interacting Agents (WEHIA 2005), 13-15 June 2005, University of Essex, UK.
We investigate the complex relationships between countries in the Eurovision Song Contest, by recasting past voting data in terms of a dynamical network. Despite the British tendency to feel distant from Europe, our analysis shows that the U.K. is remarkably compatible, or 'in tune', with other European countries. Equally surprising is our finding that some other core countries, most notably France, are significantly 'out of tune' with the rest of Europe. In addition, our analysis enables us to confirm a widely-held belief that there are unofficial cliques of countries -- however these cliques are not always the expected ones, nor can their existence be explained solely on the grounds of geographical proximity. The complexity in this system emerges via the group 'self-assessment' process, and in the absence of any central controller. One might therefore speculate that such complexity is representative of many real-world situations in which groups of 'agents' establish their own inter-relationships and hence ultimately decide their own fate. Possible examples include groups of individuals, societies, political groups or even governments.