Canonical and path integral quantisation of string cosmology models

We discuss the quantisation of a class of string cosmology models that are characterized by scale factor duality invariance. We compute the amplitudes for the full set of classically allowed and forbidden transitions by applying the reduce phase space and the path integral methods. We show that these approaches are consistent. The path integral calculation clarifies the meaning of the instanton-like behaviour of the transition amplitudes that has been first pointed out in previous investigations.Comment: 18 pages,2 eps figures, LaTeX2e, macro files included (epsf.tex,epsf.sty), macros of Classical and Quantum Gravity used; accepted for publication on Classical and Quantum Gravit

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

Partition MCMC for inference on acyclic digraphs

Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphical models. MCMC methods, notably structure MCMC, to sample graphs from the posterior distribution given the data are probably the only viable option for Bayesian model averaging. Score modularity and restrictions on the number of parents of each node allow the graphs to be grouped into larger collections, which can be scored as a whole to improve the chain's convergence. Current examples of algorithms taking advantage of grouping are the biased order MCMC, which acts on the alternative space of permuted triangular matrices, and non ergodic edge reversal moves. Here we propose a novel algorithm, which employs the underlying combinatorial structure of DAGs to define a new grouping. As a result convergence is improved compared to structure MCMC, while still retaining the property of producing an unbiased sample. Finally the method can be combined with edge reversal moves to improve the sampler further.Comment: Revised version. 34 pages, 16 figures. R code available at https://github.com/annlia/partitionMCM

Action Plans and Socio-Economic Evolutionary Change

An important challenge to evolutionary economics consists of how to tackle with the dramatic tension between purposeful human action and the ‘blindness’ of evolutionary processes. On the one hand, economic action, if rational, has to be planned (which implies purposeful ordering of the means used to achieve objectives). On the other hand, an evolutionary process involves both the emergence of novelties (both intended innovations and unintended consequences of actions) and properties that manifest at meso and macro levels. Some recent papers have insisted on these issues. However, few analytical tools are yet available to cope with both, the analysis of intended dynamic action and ‘blind’ evolution. In this paper we propose the so-called ‘action plan approach’, a theoretical framework which could be useful for this task. The development of tools that permit us to analyze how individuals construct their plans, the projective (conjectural) and interactive nature of action, and the learning processes involved in ‘planning and acting’, may help us identifying and understanding new sources of complexity of economic processes. The close relationship of the ‘action plan approach’ with other systemic conceptual approaches is also highlighted.connections, action plans; novelty; intentionality; evolutionary economic process

Topological phase transitions in the 1D multichannel Dirac equation with random mass and a random matrix model

We establish the connection between a multichannel disordered model --the 1D Dirac equation with $N\times N$ matricial random mass-- and a random matrix model corresponding to a deformation of the Laguerre ensemble. This allows us to derive exact determinantal representations for the density of states and identify its low energy ($\varepsilon\to0$) behaviour $\rho(\varepsilon)\sim|\varepsilon|^{\alpha-1}$. The vanishing of the exponent $\alpha$ for $N$ specific values of the averaged mass over disorder ratio corresponds to $N$ phase transitions of topological nature characterised by the change of a quantum number (Witten index) which is deduced straightforwardly in the matrix model.Comment: 7+4 pages, 9+1 pdf figures ; v2: paper reorganised, discussion of non-isotropic case adde

Economic Order Quantity (EOQ) Inventory Management - Essays in Experimental Economics

This thesis consists of six chapters to experimentally study aspects of how levels of individuals’ cognitive stress, cognitive ability and self-regulatory resource affect their decision making under the Economics Order Quantity (EOQ) inventory management environment. In Chapter 3 we use laboratory experiments to evaluate the effects of cognitive stress on inventory management decisions in a finite horizon economic order quantity model. We manipulate two sources of cognitive stress. First, we vary participants’ participation in a pin memorisation task. This exogenously increases cognitive load. Second, we introduce an intervention to reduce cognitive stress by only allowing participants to order when inventory is depleted. This intervention restricts the policy choice set. Increases in cognitive load negatively impact earnings with and without the intervention, with these impacts occurring in the first year. Participants’ in all treatments tend to adopt near optimal policies. However, only in the intervention and low cognitive load treatment do the majority of choices reach the optimal policy. Our results suggest that higher levels of multitasking lead to lower initial performance when taking on new product lines, and that the benefits of providing support and task simplicity are greatest when the task is first assigned. In Chapter 4 we use laboratory experiments to evaluate the effects of individuals’ cognitive abilities on their behaviour in a finite horizon economic order quantity model. Participants’ abilities to balance intuitive judgement with cognitive deliberations are measured by the Cognitive Reflection Test (CRT). Participants then complete a sequence of five “annual” inventory management tasks with monthly ordering decisions. Our results show that participants with higher CRT scores on average earn greater profit and choose more effective inventory management policies. However these gaps are transitory as participants with lower CRT scores exhibit faster learning. We also find a significant gender effect on CRT scores. This suggests hiring practices incorporating CRT type of instruments can lead to an unjustified bias. In Chapter 5 we use laboratory experiments to evaluate the effects of individuals’ ability to self- regulate on inventory management decisions in a finite horizon economic order quantity model. An ego depletion task is implemented aiming to diminish one’s self-regulatory resources. From a psychological point of view, self-control is impaired when the mental resource has been used up over effortful control of responses. In our experiment, participants complete an ego depletion task followed by a sequence of five “annual” inventory management tasks with monthly ordering decisions. Our results show there is no obvious treatment effect on participants’ self-regulation ability