Modelling and Simulations for Tourism and Hospitality

An Introduction

Jacopo A. Baggio, Rodolfo Baggio

• Publisher: Channel View Publications
• Year: 2020
• Print ISBN: 9781845417413
• eBook ISBN: 9781845417444
• Topic: Business
• Subtopic: Hospitality, Travel & Tourism Industry
• Index: Business

This book aims to shed light on the use of various modelling tools and simulation techniques in the domains of tourism and hospitality. It offers an essential introduction to the most popular methods used for modelling and simulating systems and phenomena of interest, and an overview of these techniques and methods. The main concept of each technique and method is examined and case studies and links to free online tutorials and other helpful resources are provided. The volume aims to encourage students, researchers and practitioners in tourism and hospitality to enhance and enrich their toolbox in order to achieve a better and more profound knowledge of their field.

1 Systems and Tourism Systems

Introduction

In this initial chapter, we set out the conceptual bases for the domain of modelling and simulations. Understanding how systems are structured and the relationships between their elements, and the possibility to exploit this understanding is the essence of scientific method. Study outcomes have allowed us to better realise how many phenomena have progressed and improved our abilities (even if sometimes limited) to predict future patterns. These enquiries have also allowed us to find similarities in different situations, thereby extending our ability to describe events and solve problems. Probably more than any other human activity, tourism is a complex phenomenon composed of different entities (companies, groups, individuals, etc.) and resources interacting in non-trivial ways to satisfy the needs and wishes of its users, the tourists.

Managing and governing this phenomenon and the systems that are part of it requires approaches that differ from traditional perspectives, and attempts need to be made at experimenting for possible solutions or arrangements that cannot be obtained by ‘experimenting’ in real life. Thus, the need for a set of ‘artificial’ ways to achieve these objectives has led to the building of numeric and computerised models that form the basis for simulating different settings. These, in turn, pose the necessity for a systemic holistic view, which is more suitable than traditional reductionist approaches. This perspective is rooted in the research tradition of what is today known as complexity science (Bertuglia & Vaio, 2005; Edmonds, 2000).

The main object of our studies is a system: ‘an interconnected set of elements that is coherently organized in a way that achieves something’ (Meadows, 2008: 11). This definition accurately describes the three important components – the elements, the interconnections between them and the function or purpose of a system.

In the tourism and hospitality domains, the set of interconnected actors, from travel agencies to accommodation entities, from services such as restaurants and private tours to transportation, and public actors as well as associations and the local population form a system with the objective to promote or favour tourism in a specific destination to increase the social and economic wealth of the entities involved. If we think of tourism as a system, then the behaviour of the overall system depends on how all its parts (accommodation, services, transportation, etc.) interact and exchange information, financial flow, knowledge and strategies.

Towards a Systemic View: A Short History

As will become clear in the following pages, a system is more than just the sum of its parts. Usually it is a dynamic entity that may exhibit adaptive and evolutionary behaviour. Therefore, it is important to consider a system in its entirety, to shift the attention from the parts to the whole.

In the course of its history, our civilisation has set up and refined a relatively standard way of studying a phenomenon, tackling an issue or solving a problem. However, in many cases, the standard way is modified by individual convictions and viewpoints that, even if seldom defined completely or coherently, may have wide effects. Moreover, personal philosophical and epistemological beliefs have always played a crucial role in the history of science, and in many cases have greatly influenced the development of ideas and knowledge (Baggio, 2013).

The general approach is composed of a series of phases: (i) define an objective and the object of study; (ii) decide whether the knowledge and methods are sufficient to address it; (iii) explore what and how others have produced in comparable cases; (iv) plan and collect empirical evidence; (v) derive the appropriate conclusions; and, finally, (vi) outline an action to meet the aims of the work conducted. In doing so, researchers use a vast array of specific techniques, epistemological positions and philosophical beliefs (Losee, 2001).

In this scenario, one element has historically been well supported and accepted. When facing a big problem, a large system or a convoluted phenomenon, the best line of attack is to split it into smaller parts that can be more easily managed. Once all the partial results have been obtained, they can be recomposed to find a general solution. This notion is known as reductionism. It can be summarised in the words of René Descartes (1637) who formalised the idea. In Discourse on Method (1637: part II), he states that it is necessary ‘to divide each of the difficulties under examination into as many parts as possible, and as might be necessary for its adequate solution’.

Moreover, in the Regulae ad directionem ingenii (rules for the direction of the mind), Descartes (1701) clearly states in rule V that ‘Method consists entirely in the order and disposition of the objects towards which our mental vision must be directed if we would find out any truth. We shall comply with it exactly if we reduce involved and obscure propositions step by step to those that are simpler, and then starting with the intuitive apprehension of all those that are absolutely simple, attempt to ascend to the knowledge of all others by precisely similar steps’; and in rule XIII that ‘If we are to understand a problem perfectly, we must free it from any superfluous conceptions, reduce it to its simplest terms, and by process of enumeration, split it up into its smallest possible parts’.

Reductionism, however, has a much longer history. It is rooted in ideas and concepts that evolved from the pre-Socratic attempts to find the universal principles that would explain nature and the quest for the ultimate constituents of matter. The whole Western tradition then elaborated on these concepts that were admirably distilled in the 16th and 17th centuries. Copernicus, Galileo, Descartes, Bacon and Kepler came to a rigorous formulation of the method needed to give a truthful meaning to science. This work was very effectively refined by Isaac Newton (1687) in his Philosophiae Naturalis Principia Mathematica. The book was so successful and so widely distributed that scholars of all disciplines started to apply the same ideas to their own field of study, especially in those domains that did not have a strong empirical tradition such as the study of human societies and actions.

The reasons for this wide influence were, essentially, the coherence and apparent completeness of the Newtonian proposal coupled with its agreement with intuition and common sense. In the following years, many scientists tried to extend this perspective to other environments. Scholars such as Thomas Hobbes, David Hume, Adolphe Quetelet and Auguste Comte worked with the objective to explain aggregate human behaviour using analogies from the world of physics, and employing its laws. Vilfredo Pareto and Adam Smith, for example, presented and agreed on a ‘utilitarian’ view of the social world, which suggests that the use-value of any good can be fully reflected in its exchange-value (price). With these bases, they tried to adapt the mechanical paradigm to the field of economics. The idea of defining universal laws, setting mathematical analytic expressions and formulating gravity models or terms such as equilibrium is directly derived from the Principia.

The universality of Newton’s proposals, however, was questioned when the scientific community began to realise that going beyond simple individual objects created a number of additional variables due to mutual interactions, so that solutions to even simple-looking problems could not be easily obtained unless the ‘finer details’ in the mathematical formulation were disregarded and descriptions limited to a simplified and linearised description.

For example, the gravitational theory was accurate in dealing with simple sets of objects, but failed when applied to more numerous assemblies. The motion of planets in the solar system was initially well described, but some deviations, such as the curious perturbations observed in Mercury’s orbit, could not find a proper place in the model. Deeper investigations showed that an increase in the number of bodies in a gravitational system, made the motion of the different elements almost unpredictable. Poincaré (1884) eventually realised that even a small three-body system can produce complicated outcomes and that the equations describing it become extremely difficult to determine and practically unsolvable. The stability conditions for equilibrium in the motion of a system were later studied and characterised by Lyapunov (1892). He provided the first evidence of the fact that, in some cases, even minor changes in initial conditions, described by deterministic relationships, can result in widely differing system evolutionary trajectories. This is what we term dynamical instability or sensitivity to initial conditions, and today we identify as chaos.

The issue of dealing with a system composed of ma...

Table of contents

• Cover-Page
• Half-Title
• series
• Title
• Copyright
• Contents
• Figures and Tables
• Introduction
• 1 Systems and Tourism Systems
• 2 Models and Modelling
• 3 Methodological Approaches
• 4 Advanced Modelling Methods
• 5 Choosing a Modelling Method
• 6 Tourism and Hospitality Case Studies
• A Closing Remark
• Appendix 1: Further Readings
• Appendix 2: Software Programs
• Index