2,654 research outputs found
Symptoms of complexity in a tourism system
Tourism destinations behave as dynamic evolving complex systems, encompassing
numerous factors and activities which are interdependent and whose
relationships might be highly nonlinear. Traditional research in this field has
looked after a linear approach: variables and relationships are monitored in
order to forecast future outcomes with simplified models and to derive
implications for management organisations. The limitations of this approach
have become apparent in many cases, and several authors claim for a new and
different attitude.
While complex systems ideas are amongst the most promising interdisciplinary
research themes emerged in the last few decades, very little has been done so
far in the field of tourism. This paper presents a brief overview of the
complexity framework as a means to understand structures, characteristics,
relationships, and explores the implications and contributions of the
complexity literature on tourism systems. The objective is to allow the reader
to gain a deeper appreciation of this point of view.Comment: 32 pages, 3 figures, 1 table; accepted in Tourism Analysi
What Is a Macrostate? Subjective Observations and Objective Dynamics
We consider the question of whether thermodynamic macrostates are objective
consequences of dynamics, or subjective reflections of our ignorance of a
physical system. We argue that they are both; more specifically, that the set
of macrostates forms the unique maximal partition of phase space which 1) is
consistent with our observations (a subjective fact about our ability to
observe the system) and 2) obeys a Markov process (an objective fact about the
system's dynamics). We review the ideas of computational mechanics, an
information-theoretic method for finding optimal causal models of stochastic
processes, and argue that macrostates coincide with the ``causal states'' of
computational mechanics. Defining a set of macrostates thus consists of an
inductive process where we start with a given set of observables, and then
refine our partition of phase space until we reach a set of states which
predict their own future, i.e. which are Markovian. Macrostates arrived at in
this way are provably optimal statistical predictors of the future values of
our observables.Comment: 15 pages, no figure
Bits from Biology for Computational Intelligence
Computational intelligence is broadly defined as biologically-inspired
computing. Usually, inspiration is drawn from neural systems. This article
shows how to analyze neural systems using information theory to obtain
constraints that help identify the algorithms run by such systems and the
information they represent. Algorithms and representations identified
information-theoretically may then guide the design of biologically inspired
computing systems (BICS). The material covered includes the necessary
introduction to information theory and the estimation of information theoretic
quantities from neural data. We then show how to analyze the information
encoded in a system about its environment, and also discuss recent
methodological developments on the question of how much information each agent
carries about the environment either uniquely, or redundantly or
synergistically together with others. Last, we introduce the framework of local
information dynamics, where information processing is decomposed into component
processes of information storage, transfer, and modification -- locally in
space and time. We close by discussing example applications of these measures
to neural data and other complex systems
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Coupled dynamics of node and link states in complex networks: A model for language competition
Inspired by language competition processes, we present a model of coupled
evolution of node and link states. In particular, we focus on the interplay
between the use of a language and the preference or attitude of the speakers
towards it, which we model, respectively, as a property of the interactions
between speakers (a link state) and as a property of the speakers themselves (a
node state). Furthermore, we restrict our attention to the case of two socially
equivalent languages and to socially inspired network topologies based on a
mechanism of triadic closure. As opposed to most of the previous literature,
where language extinction is an inevitable outcome of the dynamics, we find a
broad range of possible asymptotic configurations, which we classify as: frozen
extinction states, frozen coexistence states, and dynamically trapped
coexistence states. Moreover, metastable coexistence states with very long
survival times and displaying a non-trivial dynamics are found to be abundant.
Interestingly, a system size scaling analysis shows, on the one hand, that the
probability of language extinction vanishes exponentially for increasing system
sizes and, on the other hand, that the time scale of survival of the
non-trivial dynamical metastable states increases linearly with the size of the
system. Thus, non-trivial dynamical coexistence is the only possible outcome
for large enough systems. Finally, we show how this coexistence is
characterized by one of the languages becoming clearly predominant while the
other one becomes increasingly confined to "ghetto-like" structures: small
groups of bilingual speakers arranged in triangles, with a strong preference
for the minority language, and using it for their intra-group interactions
while they switch to the predominant language for communications with the rest
of the population.Comment: 21 pages, 15 figure
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