Analysing Socio-Technical Systems Through Social Simulation

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F

ACULDADE DE

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NGENHARIA DA

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NIVERSIDADE DO

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ORTO

Analysing Socio-Technical Systems

Through Social Simulation

Maria Eduarda Santos Cunha

Mestrado Integrado em Engenharia Informática e Computação Supervisor: Rosaldo Rossetti

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Analysing Socio-Technical Systems Through Social

Simulation

Maria Eduarda Santos Cunha

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Abstract

Socio-technical systems pertain to the theory related to social aspects, people and society, and to the technical aspects of structures and organisational processes. Usually, it is considered an ap-proach to the specification and development of complex mechanisms of organisational work which recognises the interaction between people and technology in their work environment. The term also refers to the interaction between society’s complex infrastructures and human behaviour. In this regard, society itself and most of its substructures can be considered complex socio-technical systems. In this perspective, organizational structures can be interpreted as being formed by two basic layers: i) a social network; and ii) a structure composed of processes, tools, and techniques. Whereas the former basically endows the protocols of human interactions, the latter supports the fulfillment of their cooperation and collaboration endeavours.

Therefore, analysis of such complex structures is hampered by the uncertainty inherent to human decision-making processes, for which its endogenous and exogenous factors are still nei-ther known nor understood very well. Recently, the use of social simulation techniques has been widely suggested as an appropriate approach for the analysis of socio-technical systems, allowing the identification of the externalities which influence their emergent behaviour and social practices which condition their performance. This is a relatively new and uncharted territory considering that, although existing literature acknowledges the need to focus on the social aspect of these sys-tems, it is still widely untapped at, with researchers having mainly focused on the technical aspect so far.

In this project, we intend to develop a social simulation meta-model particularly suitable to represent socio-technical systems, considering both their social network and the infrastructure un-derlying the implementation of their processes, tools, and techniques. The proposed meta-model will support the analysis of social coordination policies towards the improvement of a given set of characteristics of the system, therefore allowing for the identification of key social practices influencing its performance in practical applications. Some of such application domains include complex organizations where interactions between people and technology are evident, from indus-trial organizations and human-assisted automated processes to large-scale systems, such as smart cities equipped with a diverse array of sensors and services. In this context, resorting to appro-priate metaphors to represent human cognition, decision-making, and ultimately behaviour is an imperative part in the process of devising the meta-model ambitioned in this work, as well as is the effect of technology on its transformation.

Hence, we propose to implement a social simulation meta-model in NetLogo suitable to rep-resent the complex socio-technical system of a campaign hospital, created to support existing healthcare facilities as a response to the demands created by the COVID-19 pandemic. This virus outbreak has proven to be a challenge for most communities, requiring them to adapt to a newfound reality. Cities now need to accommodate the circulation of their populations in a safe manner, dealing with economic repercussions, and avoiding the over-saturation of the countries’ healthcare facilities. So far, the latter has happened with dramatic consequences in terms of loss

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of human lives. With this model we intend to support the analysis of social coordination poli-cies towards the improvement of a given set of characteristics of the system. By considering both technical and social dimensions, we expect to gain insights into how certain aspects such as the collaborativeness of patients or the nature of staff might affect the healing speed of patients and, in a parallel manner, the efficiency of the campaign hospital as a whole. Ultimately, all emergent behaviour should provide useful insights allowing for the identification of key social practices influencing its performance.

Keywords: Socio-technical systems, Social Simulation, Artificial societies, HCI theory, Social Engineering, Agent-based modelling, Agent-based simulation, Multi-agent systems, Artificial In-telligence, Decision support systems

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Resumo

Os sistemas sociotécnicos dizem respeito à teoria relacionada com os aspectos sociais, pessoas e sociedade, e aos aspectos técnicos das estruturas e processos organizacionais. Normalmente, é considerada uma abordagem para a especificação e desenvolvimento de mecanismos complexos de trabalho organizacional que reconhece a interação entre pessoas e tecnologia no seu ambiente de trabalho. O termo também se refere à interação entre as infraestruturas complexas da sociedade e o comportamento humano. Nesse sentido, a própria sociedade e muitas das suas subestruturas podem ser consideradas sistemas sociotécnicos complexos. Nesta perspectiva, as estruturas orga-nizacionais podem ser interpretadas como sendo formadas por duas camadas básicas: i) uma rede social; e ii) uma estrutura composta por processos, ferramentas e técnicas. Enquanto o primeiro dota basicamente os protocolos de interações humanas, o segundo apoia a realização dos seus esforços de cooperação e colaboração.

Assim, a análise de estruturas tão complexas é dificultada pela incerteza inerente aos processos de tomada de decisão humana, para os quais os seus fatores endógenos e exógenos ainda não são conhecidos ou muito bem compreendidos. Recentemente, o uso de técnicas de simulação social tem sido amplamente sugerido como uma abordagem apropriada para a análise de sistemas so-ciotécnicos, permitindo a identificação das externalidades que influenciam o seu comportamento emergente e das práticas sociais que condicionam o seu desempenho. Este é um território relati-vamente novo, considerando que, embora a literatura existente reconheça a necessidade de focar no aspecto social destes sistemas, ainda se encontra amplamente inexplorado, com a comunidade científica tendo-se concentrando maioritariamente no aspecto técnico até agora.

Com este projeto, pretendemos desenvolver um metamodelo de simulação social particular-mente adequado para representar sistemas sociotécnicos, considerando tanto a sua rede social como a infraestrutura subjacente à implementação dos seus processos, ferramentas e técnicas. O metamodelo proposto apoiará a análise de políticas de coordenação social para o aprimoramento de um determinado conjunto de características do sistema, permitindo, assim, a identificação das principais práticas sociais que influenciam o seu desempenho em aplicações práticas. Alguns desses domínios de aplicação incluem organizações complexas onde as interações entre pessoas e tecnologia são evidentes, desde organizações industriais e processos automatizados assistidos por humanos até sistemas de grande escala, como cidades inteligentes equipadas com uma gama diversificada de sensores e serviços. Neste contexto, o recurso a metáforas adequadas para repre-sentar a cognição humana, tomadas de decisão e, em última instância, comportamento é uma parte fundamental no processo de concepção do metamodelo ambicionado neste trabalho, assim como o efeito da tecnologia na sua transformação.

Propomos, então, implementar um metamodelo de simulação social em NetLogo adequado para representar o complexo sistema sociotécnico de um hospital de campanha, criado para apoiar as unidades de saúde existentes como uma resposta às necessidades criadas pela pandemia do COVID-19. Este surto de vírus provou-se um desafio para a maioria das comunidades, exigindo que elas se adaptassem a uma nova realidade. Agora, as cidades precisam de acomodar a

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lação das suas populações de maneira segura, lidando com as repercussões económicas e evitando a sobressaturação das unidades de saúde dos vários países. Até agora, esta última situação aconte-ceu com consequências dramáticas em termos de perda de vidas humanas. Com este modelo pre-tendemos permitir a análise de políticas de coordenação social para a melhoria de um determinado conjunto de características do sistema. Ao considerar as dimensões técnica e social, esperamos obter informação relevante sobre como certos aspectos, como a colaboração dos pacientes ou a natureza da equipa médica, podem afetar a velocidade de cura dos pacientes e, de forma paralela, a eficiência do hospital de campanha como um todo. Em última análise, todo o comportamento emergente deve fornecer percepções úteis, permitindo a identificação das principais práticas soci-ais que influenciam o seu desempenho.

Keywords: Sistemas socio-técnicos, Simulação social, Sociedades artificiais, Interação Humano-Computador, Engenharia Social, Modelação baseada em agentes, Simulação basead em agentes, sistemas multi-agentes, Inteligência Artificial, Sistemas de suporte à decisão

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Acknowledgements

I would like to express my gratitude to my supervisor Rosaldo Rossetti for his invaluable insight and ability to always point me in the right direction throughout this project; my co-supervisor Pedro Campos for his recommendations and corrections concerning all the aspects that would otherwise have been overlooked; my fellow colleagues and alumni who always had the most helpful suggestions to my queries; and my family who never doubted me and helped me keep my eyes on the prize.

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“Hamilton drew first position Looking, to the world, like a man on a mission”

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List of Figures

2.1 Timeline of the first appearances of the term "Socio-technical systems". . . 4

2.2 Concept map of a STS built on [41] by [8]. . . 6

2.3 Activity Diagram extracted from [9]. . . 9

2.4 Steps to develop a model that reproduces a real system through KISS strategy extracted from [31]. . . 14

2.5 Steps to develop a model that reproduces a real system through KIDS strategy extracted from [31]. . . 14

2.6 Steps to develop a model that reproduces a real system through TAPAS strategy extracted from [31]. . . 14

3.1 Conceptual class diagram of the problem. . . 18

3.2 Stakeholders involvement throughout the system layers. . . 20

4.1 Implemented NetLogo interface with legend numbers. . . 26

4.2 Average occupancy rate plot for scenario 1’s average and worst cases. . . 29

4.3 Average waiting time plot for scenario 1’s average and worst cases. . . 30

4.4 Average occupancy rate plot for scenario 2’s average case. . . 32

4.5 Average occupancy rate plot for scenario 2’s worst case. . . 33

4.6 Average waiting time plot for scenario 2’s average case. . . 34

4.7 Average waiting time plot for scenario 2’s worst case. . . 35

4.8 Average visit duration plot for scenario 2’s average case. . . 36

4.9 Average visit duration plot for scenario 2’s worst case. . . 37

4.10 Average single visit duration plot for scenario 2’s average case. . . 37

4.11 Average single visit duration plot for scenario 2’s worst case. . . 38

4.12 Average occupancy rate plot for scenario 3’s average case. . . 38

4.13 Average occupancy rate plot for scenario 3’s worst case. . . 39

4.14 Average waiting time plot for scenario 3’s average case. . . 39

4.15 Average waiting time plot for scenario 3’s worst case. . . 40

4.16 Average visit duration plot for scenario 3’s average case. . . 40

4.17 Average visit duration plot for scenario 3’s worst case. . . 41

4.18 Average single visit duration plot for scenario 3’s average case. . . 41

4.19 Average single visit duration plot for scenario 3’s worst case. . . 42

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List of Tables

2.1 From SAA to a Multi-agent Simulation Model based on [50]. . . 8

2.2 Comparison between the 3 iterative modelling strategies KISS, KIDS, and TAPAS extracted from [31]. . . 10

2.3 Direct correlation between STS and ABMS . . . 13

3.1 Elements of STS directly derived from the ATC examples. . . 19

4.1 State variables of ODD entities . . . 25

4.2 Set up of scenario 1’s average and worst cases. . . 28

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Abbreviations

ABDS Agent-based Decision Support ABM Agent-based modelling

ABMS Agent-based modelling and simulation ABS Agent-based simulation

AI Artificial Intelligence ATC Air Traffic Control BDI Belief-Desire-Intention DGS Directorate-General of Health DSS Decision Support System

IASAM Integrated Acceptance and Sustainability Assessment Methodology ICT Information and Communications Technology

MAS Multi-agent System MFA Material Flow Analysis

ODD Overview, Design concepts, Details OOP Object Oriented Programming SAA Structural Agent Analysis SF Structural Factors

SP Stated-preference STS Socio-technical Systems TCM Team Coordination Model

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Chapter 1

Introduction

1.1 Context and Motivation

Contemporary societies are witnessing an increasing use of information and communications tech-nology (ICT) in all dimensions of their daily lives, from professional activities to social interac-tions. These scenarios give rise to the concept of socio-technical systems (STS).

STS pertain to the theory related to social aspects, people and society, and to the technical aspects of structures and organisational processes, i.e., they are systems involving both social and technical aspects, through the interaction of people and technology, or society’s complex infrastructures and human behaviour. In keeping with this definition, society and most of its underlying substructures can be considered complex STS.

The uncertainty inherent to human decision-making processes hinders the analysis of STS, alongside the fact that the factors affecting these systems remain unknown or not sufficiently understood.

An appropriate understanding of all factors affecting the underlying relationships of socio-technical systems may directly contribute to the design of efficient operational policies and coor-dination strategies towards maximising the performance of such systems.

1.2 Problem and Objectives

At the moment, there is not a set meta-model or methodology to approach these STS in a way they can be easily modelled and analysed, with existing attempts focusing predominantly on the technical aspect in detriment of the social.

The aim is to devise a social simulation meta-model, capturing the main operators, relation-ships, and concepts to support the analysis of STS. Such a meta-model will allow for STS policy-making and incentive designs. The main idea is to enable rapid testing of various operational policies or coordination strategies to grasp their efficiency in a particular scenario and, eventu-ally, make a more informed choice to maximise the performance of the system, and enable the identification of key social practices influencing a system’s performance in practical applications.

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2 Introduction

To achieve the proposed aim, the following specific objectives are identified:

• To identify a consensual definition of STS to be considered in this proposal to limit its scope; • To elicit social requirements of a STS;

• To develop an adequate MAS methodology to map operators, relationships, and concepts; • To demonstrate the appropriateness of the model devised through the choice of a case study

to which the model will be applied.

1.3 Document Structure

The remaining document is organized in three main chapters: chapter 2 Literature Review is the review of the literature on the state of the art, with a focus on the subjects of Socio-technical Systems, Agent-based Modelling and Simulation, and Social Simulation; chapter 3Towards a Reference Model of Socio-Technical Systems provides inspirational examples of STS and con-cepts drawn from those, the problem’s formalisation, and a STS reference architecture;chapter 4

Results and Analysis describes the implementation of the model, the run simulation scenarios, alongside the assessment and discussion of the results obtained; The last chapter,chapter 5 Con-clusions performs a general overview of the work, presents its expected contributions, and builds on further developments and future work.

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Chapter 2

Literature Review

In this chapter, there is an overview and analysis of the literature covering from the background to the state of the art in regard to the concepts which are paramount to this dissertation - Socio-technical Systems, Agent-based modelling and simulation, and Social Simulation, explored in2.1

Background. While2.2Related Work outlines some approaches in the line of modeling socio-technical systems.

2.1 Background

2.1.1 Socio-technical Systems

2.1.1.1 Genesis/Seminal Work

Long(2013) states the term STS was coined by Eric Trist, Ken Bamforth, and Fred Emery, in the mid-twentieth century, during World War II, with respect to their work with miners from the En-glish coal mines, at the Tavistock Institute in London [32], in Trist and Bamforth(1951)’s Some so-cial and psychological consequences of the longwall method of coal-getting[57], Emery(1959)’s Characteristics of Socio-Technical Systems[18], and Trist(1981)’s The evolution of socio-technical systems: a conceptual framework and an action research programme[56].

A look into history suggests this notion of STS came to be only in the mid-nineties with the invention of Ford’s assembly line in 1913 and Taylor’s scientific management in 1911 generating a need for an approach to organizational design which integrated social and technical aspects. The implications of the assembly line and Taylorism, although major history breakthroughs influencing society to this day, brought about new issues as well, such as predictable tasks, strict rules, and strictly defined hierarchies amounting to uninspired workers or big factories working at great pace precluding a sense of familiarity within the staff. It became apparent that efficiency was not solely based on optimizing processes, but ensuring the well-being of the employee, in other words, to consider the social components as well as the technical (see figure2.1).

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4 Literature Review

Figure 2.1: Timeline of the first appearances of the term "Socio-technical systems".

2.1.1.2 Main Definitions, Consensual Definitions, Unified Definition The definition of STS is fairly consensual throughout literature.

Omicini and Zambonelli(2015) define them as systems with “(...) up to millions of interacting components, lacking central control, mixing humans and artificial components (...)” [38]. Appel-baum(1997) adds that, considering that in STS “(...) the social and technical elements must work together to accomplish tasks, work systems produce both physical products and social/psycholog-ical outcomes.” [4]. Additionally, Long(2013) sees STS as an approach to complex organizational design since they refer to the interaction between society’s complex infrastructures and human behaviour [32].

Considering the characteristics of STS, it is possible to draw the conclusion that modern organ-isations are STS. Aslanyan et al.(2015) mentions this very same idea by stating modern organisa-tions are STS since they consist of physical infrastructures, human actors, policies, and processes [5]. This correlation between STS and existing organisations brings about the need for faithful representation at modeling level. Accordingly, Sommervile(2014) points out the nature of STS as systems governed, on the one hand, by organizational policies and rules and, on the other, which might also be affected by external constraints, such as a national law, for example [54].

Whitworth(2009) dives further into what makes up such a system and divides it in four levels: hardware, software, human, and organizational, the first two being technical and the last two social. He characterises them as systems that “(...) integrate the multiple requirements of system perfor-mance at higher (...) levels, where each level builds upon the previous (...)”, “(...) integrate the

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2.1 Background 5

multiple channels of human communication”, and “reflect rights in overlapping social groups.” In addition, he mentions key concepts such as justice, legitimate interaction and opposing antisocial acts, which should be considered when designing such a system and others like rights, leadership, roles, and democracy which are still poorly represented in technical design [59]. Whitworth is not the only author to recognise this lack of representation. Sharing the same viewpoint, authors such as Aizstrauta and Ginters(2017) highlight how not only the complexity of STS difficults the development, definition of requirements, testing and analysis of these systems, but also adds to the need of approaching their field of study as highly multidisciplinary [2]. Hence, it is acknowledged that the social aspects of STS are not being represented in full, but there is a reason behind this gap: the difficulty of tackling such a feat.

In short, all these authors mention similar ideas: components at large scale, social and techno-logical interaction, decentralization, and the lack of representation in existing models of basic key concepts present in real life that determine social interactions.

In this respect of systems that truly model real life and social interactions, a particular concept is to be noted, the idea of Social Practices (SP). Authors such as Dignum(2018) define social practices as everyday practices and the way they are usually performed by the generality of society [15], Reckwitz(2002) follows on this idea stating they are accepted ways of doing things routinized over time [44], and Schatzki(2002) views them as organized nexus of actions performed by agents: “Individuals, thanks to their knowledge, understanding, and expectations about situations, perform open, temporally unfolding nexuses of actions” [48].

The importance of SP resides in the following: SP not only condition actors’ behaviors but also allow those same actors to expect certain actions from other actors. For this reason, they can be used to simplify the amount of actions an actors has to take into account or solve impasses when an actor could be in standby awaiting an action from another actor.

Authors such as [52], [30], and [15] have elaborated further on the subject: Shove et al.(2012) explains that SP, as patterns, are not static, as certain elements such as know-how, meanings, and purposes reconfigure and adapt [52]. This means that the use of social practices involves a constant learning in ever changing contexts, Jennings(2000) draws attention to the importance of SP as they help draw a context for interactions and if they are not considered “(...) the patterns and the outcomes of the interactions are inherently unpredictable, and predicting the behavior of the overall system based on its constituent components is extremely difficult (sometimes impossible) because of the high likelihood of emergent (and unwanted) behavior” [30], and Dignum(2018) adds that “(..) useful social practices can simplify the amount of actions that have to be planned by an actor and that it can make assumptions about interactions with other actors in the social practice without explicitly having to coordinate” [15].

Reckwitz(2002) sums up these ideas by stating that SP are “(...) the implicit, tacit or uncon-scious layer of knowledge which enables a symbolic organization of reality (...)”, “(...)lay[s] down which desires are regarded as desirable and which norms are considered to be legitimate.”, and

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6 Literature Review

Figure 2.2: Concept map of a STS built on [41] by [8].

Philipp Budka produced an interesting concept map that encompasses the ideas inherent to a STS from an anthropological point of view (see figure 2.2) [8]. It was built on Bryan Pfaffen-berger(1992)’s article Social Anthropology of Technology in which the author discussed the use of STS in an anthropological context [41].

2.1.2 Agent-based Modelling and Simulation

2.1.2.1 Main Methodologies for Modelling and Simulation of Multi-agent Systems

When modelling and simulating multi-agent systems, a recurring solution throughout literature has been the use of Agent-based Modelling (ABM) and Simulation (ABS).

Its popularity was confirmed in a survey by Gómez-Cruz et al.(2017) which found that ABS provided a “(...) robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving.” [25].

Several authors ([7]; [43]; [60]; [24]) agree that ABS is particularly well-suited when the system under study meets the following requirements:

• System is composed of various autonomous, heterogeneous components;

• Its agents act deprived of global knowledge in a local, parallel and distributed manner, which means interactions are non-linear, discontinuous and asynchronous;

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2.1 Background 7

• Its global dynamic is self-organizing and emergent, therefore displaying properties such as memory, path-dependence, temporal correlations, learning, adaptation and evolution; • Its environment is uncertain and often includes a non-reducible spatial component.

Ferscha et al.(2011) suggest ABM as an adequate approach to analyse social systems con-sidering their capability of representing individual entities and their respective interactions, while methods relying on structural or differential equations are not able to properly represent individual heterogeneous entities in large scale and their respective interactions [19]. Crooks and Heppen-stall(2012) reinforced this very same idea adding how ABM allows each agent to be modeled with a “(...) different set of properties that include perception, communication, reactivity, proactivity, flexibility, learning and adaptation” [13].

Schwarzer et al.(2018)‘s research shows that ABS models are a preferred tool for both so-cial simulations and models focusing on technical aspects [50], which according to Bandini et al.(2009) is enabled by the fact they are particularly well suited for situations where entities are autonomous and whose actions and interactions determine the overall system [6]. Considering this idea of an action or interaction which affects the overall system, Schelling(2006) and Hamann and Wörn(2008) refer to the importance of defining the micro-macro link in hybrid societies which ap-plies to STS [49][27] considering the definition of hybrid societies by Haman et al.(2016) as self-organizing, collective systems composed of different components which might be, for example, human beings interacting with and through technical systems [26] which is the very same defini-tion of STS. They explain macro-to-micro as the global behavior required and micro-to-macro as understanding the macro-behavior outcome considering a micro-behavior, which, in other words, translates into actions or interactions which affect the overall system. As a response to this need to find a means to identify how small actions within a STS affect it in the long run, Goméz-Cruz et al.(2017) state “ABS links agents’ micro-behaviors to macro-patterns that emerge from their interactions.” [25].

Cioffi(2014) states ABS is able to accurately represent both natural and artificial (man-made) environments [12]. This idea of natural and artificial environments covers the generality of STS.

Bonabeau(2002) sums up the conclusions mentioned by the other authors in three main ideas: that ABM is useful for capturing social systems due to (i) its ability to capture emergent phenom-ena, (ii) how it provides a natural description of the system and (iii) its flexibility [7].

2.1.3 Social Simulation

2.1.3.1 Main Methodologies for Social Simulations

Tsvetovat et al.(2004) argue that in order to create models of socio-technical systems which are reliable and faithful to reality a combination of AI algorithms, multi-agent systems and social simulation is needed, which “(...) results in complex simulation-oriented multi-agent systems that incorporate planning and learning algorithms, while built on an extensive model of social network

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8 Literature Review

Table 2.1: From SAA to a Multi-agent Simulation Model based on [50]. Structural Agent Analysis Multi-agent Simulation Model

agent agent

structural factor attributes

changes of structural factors perception

options of agents interaction

interaction among structural factors reasoning/decision making influence of structural factors on options reasoning/decision making

Schwarzer et al.(2018) pinpoints the belief-desire-intention (BDI) framework as widely used to model the process of human decision making in agent-based models. The foundation of BDI re-sides in three factors: the agent’s knowledge of their own state and surrounding environment, their objectives, and their plans. These make up belief, desire, and intention, respectively. In their work, they combined Material Flow Analysis (MFA) with Structural Agent Analysis (SAA). The steps followed in terms of the SAA considered literature reviews, surveys, expert interviews, and MFA methods, and were: i) Identification of relevant agents, ii) Analysis of structural factors (SF), iii) Weighing the relevance of SF, iv) Agent–structure diagram, v) Agents’ options, constraints and fa-cilitators, vi) Interferences among agents, vii) Effects of agents’ actions on structure. Afterwards, the results from SAA were implemented in a computational agent-based simulation model, using the mapping in figure2.1[50].

Caillou et al.(2015) actually developed a framework implementing a BDI architecture for ABMS. In their work, agents are modelled considering the three components of BDI and behave as follows (see figure2.3): Perceive the environment; (i) If their plan is not finished, they will continue, (ii) if it is finished and their intention is not fulfilled, they select a plan, (iii) if their in-tention is fulfilled, they select a new desire to add to their inin-tention stack. Their data was provided by a department of connection to the theme of study and they were assisted by domain-experts. This allowed them to establish the types of agent to consider, which attributes should characterize each type of agent, and the measure performances to be evaluated. Their code was implemented in GAML modelling language and to evaluate the quality of the model, they compared the simulation results obtained to the observed data, considering two indicators: the fuzzy kappa coefficient and the percent absolute deviation [9].

To the end of achieving a more generalized approach towards social simulation, in her work Klügl looks into three iterative modeling strategies:

• KISS: Starting from an over-simplified model (see figure2.4): Identify and describe the set of observable properties of the original system to be reproduced by the final model; Define a model apparently too simple for reproducing the system with all the observed properties; Determine the set of properties that are reproduced by the model through cali-bration and update model as such; Repeat the process until the final version of the model fully captures all the phenomena of the real system.

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2.1 Background 9

Figure 2.3: Activity Diagram extracted from [9].

• KIDS: Beginning with a more believable model (see figure 2.5): Define a model that contains all apparently relevant aspects of agent behavior; Use sensitivity analysis to elim-inate all blocks of behavior that are controlled by a parameter without effect on the overall outcome.

• TAPAS: Reusing existing models (see figure 2.6): Choose an existing adequate model; Implement it; Add until it fully reproduces the real system.

• Candidate-based modelling: Construct a set of alternatives which might differ according to different parameter settings or use different architectures; Calibrate and evaluate each of the candidates; Choose best model and use as basis for future research.

In her research into these methods, she also provides a comparison between them to offer a better understanding of in which situations to use each strategy (see table 2.2). Based of this table, KIDS appears to be the most adequate approach for Social Simulation of a STS, considering it

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10 Literature Review

Table 2.2: Comparison between the 3 iterative modelling strategies KISS, KIDS, and TAPAS extracted from [31].

Strategy KISS KIDS TAPAS

Candidate-based

Apt for linear models high high high high

Apt for emergent phenomena low high mid high Apt for shared-environment actors mid high mid high

Objective-orientedness high mid mid mid

Resulting minimality high mid low mid

Share of try & error procedure low mid high high

Empirical data requirement mid mid low high

Integration of previous knowledge mid high high mid

Communication support mid high mid mid

Modelling overhead mid mid low high

Required expertise in macro models high low low low Required expertise in micro models high high low high

Required tool knowledge high mid mid high

previous knowledge and communication support. Ideally, it should provide a more objective-oriented model, but the only strategy offering this at high level is KISS, which fails to deliver in terms of the remaining characteristics [31].

2.2 Related Work

2.2.1 Agent-based Models of STS

Ferscha et al.(2011) implemented a model in Netlogo intending to determine the influence of Ambient Intelligence affected agents in the normal agents in terms of finding the best exit in an emergency situation at Linz main railway station. It showed that the technologically assisted agents emerged as leaders during evacuation altering the intentions of most of the agents, even with a small population of such leaders [19].

Zoto et al.(2018) used an ABS tool to simulate cases of attack and defense at cyber security level to teach students the relevance between different conditions that make a cyber-attack and a cyber-defense effective. In their approach, they considered three main attributes, Resources, Skills, and Motivation, as the major elements of influence in the behaviour and performance of the actors [61].

Schwarzer et al.(2018) combined results from MFA and SAA, and implemented them in a MAS modeling framework to obtain a demand response system which enabled to shift or cut back on the use of electricity at residential level so as to assist an energy system’s reliability [50].

Tsvetovat et al.(2004) developed NetWatch, a multi-agent network model, where agents are cognitively and socially bounded by the knowledge they possess on themselves and others consid-ering personal history, as in line with the reality of such a system, “(...) for reasoning about the

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2.2 Related Work 11

destabilization of covert networks such as organized crime or terrorist organizations under condi-tions of uncertainty (...)” through the development of different strategies which might lessen their efficiency, adaptability, and ability to communicate or exchange resources [58].

Aizstrauta and Ginters(2017) developed a STS Integrated Acceptance and Sustainability As-sessment Methodology (IASAM) for the evaluation of new technologies, with the purpose of being a “(...) comprehensive tool to help stakeholders embrace the change brought by technology innovations and to analyse the acceptance and sustainability of emerging technologies (...)”, based on the view that technology acceptance research should not be divided from the technological, economic, and social evaluation [2].

Sun and Naveh(2004) ran a simulation to study organizational dynamics and drew the conclu-sion that when deciconclu-sions are made in non-hierarchical teams, they have better outcomes than those made in hierarchical structures, and organizations with free access to information perform better than those with restricted access [55].

[20]Forkmann et al.(2012) analysed the connection between strategic decision making and an agent’s position of power. They observed that despite dominant strategies being favoured by CEOs, they do not cardinally deliver the best results in the long run, which comes to show the mismatch between empirically derived decisions made by managers and the effect those strategies actually have on their businesses.

North et al.(2010) emphasised the need for holistic models that depict the interdependencies between consumers, retailers, and producers in consumer markets to assist in the decision making process. As an example, they developed an ABM addressing various organizational problems at Procter & Gamble to aid with organizational decisions. This successfully lead to substantial savings in operational costs [37].

Hilletofth and Lättilä(2012) investigated the advantages and disadvantages of using agent-based decision support (ABDS) systems in the supply chain context. They developed and eval-uated two ABDS systems based on existing companies, one concerning a manufacturing supply chain and the other concerning a service supply chain. With respect to this context, ABDS proved useful by allowing for experiments and what-if analysis, leading to an improved understanding of the real system. Through ABDS, it was possible to increase the versatility of system architecture, enhance supply chain visibility, and boost communication within and between organizations in the supply chain [28].

Nilsson and Darley(2006) applied ABM to logistic and manufacturing systems, with a model depicting a packaging company in the UK. In consonance with Hilletofth and Lättilä(2012)’s find-ings, their results suggested ABM provided robust and accurate what-if scenarios which could guide managers in choosing different production, warehouse, customer service, and logistics poli-cies. Withal, it saw to a better understanding of the patterns and effects which emerged in such settings [36][28].

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variables. The framework itself was based on optimization and ABM. In order to demonstrate the applicability of this model, they used an oil refinery case study as proof of concept [29].

Rojas and Giachetti(2009) delved into the collaborative processes in teams that work “(...) under well-defined but dynamic job environments”. They presented Team Coordination Model (TCM), an ABS model depicting those processes and imbuing its agents with the ability to com-municate, process tasks, and make decisions. The chosen scenario was based on the Race Com-mittee for a sailboat race and the TCM was used to analyse this Race ComCom-mittee and advise a favourable design configuration [45].

Singh et al.(2012) studied social learning and its impact on team performance by developing an ABS model which received distinct social learning modes as parameters to be observed in the simulations. Their results demonstrated that the success of the social learning strategies depended on team familiarity, such that this familiarity should increase with task complexity to ensure higher efficiency [53].

Although not explicitly dealing with STS, Almeida et al. (2012) propose a simple agent-based simulation setup using NetLogo to test with evacuation scenarios in emergency situations, such as building fires [3]. Indeed, tackling fire situations, whether indoors or outdoors, involves actors with specific and technical knowledge, who are expected to follow well orchestrated and coordinated protocols under the stress of emergency situations. Therefore, firefighters, civil protection officers, and citizens in general should be aware and empowered with the appropriate means to cope with emergency situations such as buildings on fires.

2.3 Summary

The performed literature review allowed for a better definition of key concepts, such as STS and SP. A simplified definition of STS would be a system involving both social and technical aspects, be they through the interaction of people and technology or society’s complex infrastructures and human behaviour. While SP are accepted norms and the way they are performed by the general society, which assists individuals in knowing what to expect from other individuals in certain situations.

We detected a decidedly significant gap in the field of analysis of STS, with the scientific community admitting to the need of considering the social aspects of such systems and not just the technical. However, this objective has been discussed mostly from a theoretical point of view, with not many practical cases being implemented fully. The cases presented which actually attempted to balance the social and technical components when modelling and simulating real STS were successful but not particularly scalable or general enough to be reused for cases outside the original domain.

Regardless, we now have a better understanding of the usefulness of ABM and ABS as they promise to be adequate approaches to model, simulate, and analyse these STS, considering their properties of allowing to model autonomous heterogeneous agents at large scale, alongside their asynchronous interactions, and capture emergent phenomena by determining how small actions

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2.3 Summary 13

Table 2.3: Direct correlation between STS and ABMS

STS ABMS

Actors are autonomous. Capability of representing individual au-tonomous entities.

Actors are heterogeneous. Allows each agent to be modeled with a dif-ferent set of properties.

Actors’ actions affect the overall system. Links agents’ micro-behaviors to macro-patterns that emerge from their interactions. Mixes social and technical aspects. Preferred tool for both social simulations and

models focusing on technical aspects. System is self-learning and considers social

practices.

Used for systems where global dynamic is self-organizing and emergent, therefore dis-playing properties such as memory, path-dependence, temporal correlations, learning, adaptation and evolution;

Should mimic reality. Used for systems where its agents act de-prived of global knowledge in a local, parallel and distributed manner, which means interac-tions are non-linear, discontinuous and asyn-chronous;

Used for systems structured in spatial-temporal scales;

affect the overall system. Table2.3shows a compiled view of the direct correlation between the characteristics of STS and the potential of ABMS, derived from the literature review.

Thus, scientific contribution could be offered by developing a meta-model for simulating these systems, encompassing social and technical dimensions, and ensuring a level of abstraction that

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Figure 2.4: Steps to develop a model that reproduces a real system through KISS strategy extracted from [31].

Figure 2.5: Steps to develop a model that reproduces a real system through KIDS strategy extracted from [31].

Figure 2.6: Steps to develop a model that reproduces a real system through TAPAS strategy ex-tracted from [31].

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Chapter 3

Towards a Reference Model of

Socio-technical Systems

This chapter describes the methodology behind the reference model created to fill the gap in STS analysis described in Section2.3. Firstly, we provide some examples of socio-technical interac-tions in section3.1Examples of Socio-technical Interactions that serve as examples to draw the concepts that make up the reference model. This elements are described in section3.2Elements of STS and the class-diagram model is provided in3.3Formalizing STS as well as the problem formalisation. Furthermore, a STS reference architecture is further elaborated on in3.4Towards a STS Reference Architecture.

3.1 Examples of Socio-technical Interactions

To better illustrate the concept of socio-technical interactions, some real scenarios can be drawn up. Air traffic control (ATC) is a prime example of STS, with its complex interactions among actors, processes, techniques, and technology. It is a service to prevent collisions, organize the flow of air traffic, and provide pilots with information and support. ATC is a common effort provided by ground-based air traffic controllers responsible for directing aircraft both on the ground and in the controlled airspace.

On the one hand, ATC contemplates the technical side of a STS as it relies heavily on all kinds of technology, such as to establish communication between different aircrafts, control towers and airports, monitor the position of aircrafts, calculate the most suitable route or provide aid with parameters for landing and take off. On the other hand, ATC is a service provided by humans. It is up to the involved workforce to use the technology, interpret the information provided by that same technology, and make decisions based on that information and personal experience developed over the years.

Additionally and in line with the previously established definition of STS, ATC is subject to a set of laws and norms, and similarly enforces a set of rules to ensure traffic separation, but in different situations those norms may be overrun or overlooked.

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16 Towards a Reference Model of Socio-technical Systems

Considering the definition of a STS as made up of both social and technical sides, it is im-portant to describe scenarios where this interaction between the two components is evident. The following examples depict real cases where the social aspect, deliberately or not, went against the technical, that being technical in terms of processes/techniques or technology.

Example 1 - Human vs Norm: A successful example of humans overrunning norms is the case of the US Airways Flight 1549, in 2009, where captain Chesley “Sully” Sullenberger and first officer Jeffrey Skiles successfully performed a forced water landing in the Hudson River, Manhattan, after both engines were disabled after striking a flock of geese, saving the lives of all 155 passengers. As routine for situations where flights do not proceed as planned from take off to landing, investigation ensued to confirm all the right decisions had been made by the pilots. Initially, the performed simulations suggested that the pilots should have chosen to return to the closest airport, LaGuardia, as suggested by the ATC, and avoided a dangerous landing on water. However, when accounting for the “human factor”, adding an average of 30/35 seconds for the pilots to decide what to do, simulations confirmed the odds of a safer landing on the river were higher than by having the flight redirected towards LaGuarda airport, with only 3 out of 9 sim-ulations proving successful. Captain Sullenberger wrote in his book Highest Duty: My Search for What Really Matters “I knew that if I chose to turn back across this densely populated area, I had to be certain we could make it. Once I turned toward LaGuardia, it would be an irrevocable choice. (. . . ) And attempting to reach a runway that was unreachable could have had catastrophic consequences for everyone on the airplane and who knows how many people on the ground.” and this comes to prove an additional element of our definition of a STS, a system where actors are sometimes susceptible to override norms in favor of culture, in this case represented by one of the most common values of society, the value of life.

Example 2 - Failed Human vs Norm: On the contrary, TANS Perú Flight 204 to Pucallpa Airport serves as an example in which human behavior led to a fatal crash. In 2005, following an emergency landing attempt due to bad weather, this flight crashed killing 40 out of 98 passengers and crew. A few pilot errors were identified in the final moments of this flight. The flight was run by the airline captain and former military commander Octavio Perez Palma, air force major Gonzalo Chirinos Delgado training to become first officer, and first officer Jorge Luis Pinto Panta. Despite the standard procedure for a flight where one of the pilots is a trainee being that there should be a backup pilot to occupy his position should things go awry, the captain allowed the first officer to leave the cockpit due to a broken seat belt, leaving the commands to himself and the trainee. On approach to Pucallpa Airport, the pilots were faced with a developing cold front, with torrential rain, hail, and strong winds. The first significant mistake was that, contrary to what is standard procedure, the captain decided to cross the storm instead of diverting to another airport to avoid the bad weather. The second mistake was that, when some minutes later the aircraft was caught by a downdraft, the pilots took longer than expected to realise their sudden drop in altitude as they failed to check the instruments. This was estimated to be because the pilots were virtually blind owing to shattered layers of the cockpit windshield thanks to the impact with the hail and were both trying to spot the runway outside instead of keeping an eye inside the cockpit. However,

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3.2 Elements of STS 17

once they realised the drop, they should have been able to regain altitude since pilots are taught to recognise and counteract downdrafts by pushing the propellers all the way forward to full engine power to gain speed and pulling up the nose of the plane, but neither pilot pushed the propellers. This was the third mistake likely made as a result of a sudden change of roles performed between them during the emergency. The human mistakes made during this flight highlight some key factors which might drive humans to ignore norms or make mistakes:

• While an inexperienced individual does everything by the book, experience may lead to overconfidence and cutting corners;

• In situations of crisis, people cannot be expected to perform exemplary. Mistakes are made and steps are overlooked as the person might be in panic, desperate, confused, unfocused, or even hysterical.

Example 3 - Successful Human vs Technology: In 2010, Qantas Flight 32 suffered an un-contained failure in one of its engines, four minutes after takeoff. As a result of “fatigue cracking” in a stub pipe within the engine, there was oil leakage and, consequently, an oil fire in the engine. The explosion led to many issues with nacelle, wing, fuel system, landing gear, flight controls, and engine controls. The plane’s systems were flooded with contradicting messages regarding failures and issues in the aircraft. The pilots went through all the messages for nearly an hour, relying on their extensive experience to decide which were important and which could be ignored. After get-ting a better understanding of the condition of the plane, the pilots used an on-flight performance application to calculate whether landing would be possible or not and with which parameters. Ac-cording to the computer, landing was impossible. The pilots then manipulated the information the application was using according to their experience and knowledge of the surrounding envi-ronment and the computer was able to produce a positive calculation, with a touchdown speed of about 165kt and 130m of surplus runway in a 4,000m runway. For almost two hours, the pilots made due with the commands left and performed a successful emergency landing at Changi, with no injuries to passengers, crew or people on the ground. The key factor for success in this scenario: the ability of the pilots to use their extensive experience to interpret and manipulate the technology in use.

3.2 Elements of STS

From the examples mentioned in the previous section, some general elements can be derived as constituents of STS. Foremost, a socio-technical system has at its center the agent. This agent communicates and interacts with other agents (communication protocol) and might coordinate or be coordinated by other agents. Agents can be organised in groups (organisation) and these groups can in turn interact between themselves or be composed by other smaller groups. The notions of agents coordinating other agents and organisations being made up of other

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organisa-18 Towards a Reference Model of Socio-technical Systems

Figure 3.1: Conceptual class diagram of the problem.

to qualify for any role they occupy and they perform tasks (an activity that can in turn be com-posed by other activities) in line with their role and which require resources provided by their organisation. Feedstock, consumables, and tools sum up any kind of resource. Agents as well as organisations are subject to norms and influenced by culture and there is a fine line between how these two elements interact as this is where a big part of the human factor plays in.

Table3.1provides a general overview of the elements of STS directly derived from the ATC examples provided in the previous section, and the diagram3.1represents the basic structure and relations between the classes of the problem.

3.3 Conceptualising STS

3.3.1 Problem Formalisation

Consider that the system is defined by the following sets and functions:

A= {a1,...,an}, n ≥ 1 and ai,aj∈A : i6=j, represents the set of agents of the system with aiand

aj representing two different agents.

t(ai, aj) is the function that establishes the technical relationship between agents ai and aj,

e.g., agent ai’s authority over aj.

s(ai, aj) is the function that establishes the social relationship between agents ai and aj, e.g.,

agents aiand ajare friends or agent aiis agent aj’s brother.

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3.4 Towards a STS Reference Architecture 19

Table 3.1: Elements of STS directly derived from the ATC examples. General Element of STS Element in ATC scenario

Agent Any of the individuals mentioned

Agent coordinates other agents Hierarchy among pilots such as captain and first of-ficer

Communication protocol between agents

Pilots communicate between themselves

Organisation (made up of agents) Different branches at ATC are made up of sets of different people such as pilots, technicians at control tower...

Organisations interacts between them-selves

Different branches of ATC communicate among themselves

Organisation composed by organisa-tions

Hierarchy inside ATC levels

Organisation owns resources All the ATC branches and groups require resources to perform

Organizations and agents subject to norms

Both groups and the individual are subject to a set of norms that dictate their work and behavior

Organizations and agents subject to culture

Both groups and the individual are influenced by culture in their work and behavior

Agent has a role e.g. pilot, first officer, captain...

Agent performs activity Individuals have tasks assigned to them in their work Role determines skills needed Individuals must qualify for their roles (e.g. a pilot

must know how to fly a plane)

Activity made up of other activities Flying involves several other tasks such as checking controllers, adjusting commands...

Pt is the performance measure of the technical features of A, whereas Psis the set of

perfor-mance measures influenced by social aspects verified in A.

Our problem is to test different policies Π so as to improve both Pt and Ps.

In order to evaluate the performance of the meta-model, two types of performance measures must be considered, performance measures characteristic of STS (e.g. number of interactions) and performance measures relating to the application domain.

3.4 Towards a STS Reference Architecture

The architecture herein proposed is inspired in the concept of artificial systems, which resort to agent-based modelling and artificial societies to study, analyse, and improve the performance of STS, such as mobility for instance [46, 47]. The proposed approach consists of a six layer structure: Decision-support layer, Abstraction layer, Application Domain layer, Social Simulation Model layer, Data Abstraction layer, and System Monitoring layer.

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20 Towards a Reference Model of Socio-technical Systems

Figure 3.2: Stakeholders involvement throughout the system layers.

terms as a dashboard featuring visualisation, different analytics, and recommendation tools. • The Services layer consists of mapping the meta-model concepts into application concepts; • The Domain Abstraction layer is essentially the meta-model abstraction layer considering

the main operators, relationships, and concepts;

• The Social Simulation Model layer is the one which supports the actual simulation execu-tion, receiving as input data concerning the operation policies to be tested and returning as output the performance measure variables.

• The Data Abstraction layer is where data is integrated; we foresee the implementation of data fusion functionalities here.

• The System Monitoring layer is where different monitoring tools are put in place to con-tinuously observe the system. It might include approaches such as different sensor net-works, naturalistic data collection, self-evaluation approaches, stated-preference (SP) ques-tionnaires, and so forth.

In STS, the stakeholders can be members, managers, and auditors. Members and managers are internal to the system, with members only needing information concerning themselves and managers requiring additional information to perform management tasks. On the other hand, auditors are external to the system and, therefore, need access to all layers to regulate the operation of the system, dictate environment norms, offer advice, and so on. The involvement of these stakeholders throughout the different system layers is visually represented in figure3.2.

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3.5 Summary 21

3.5 Summary

STS, regardless of their domain, can be generalised into some common key concepts: agents who communicate/interact (communication protocols) and can be organised into groups with associ-ated hierarchies and which might be nested into other smaller groups. Each agent possesses a set of skills and performs activities according to their role, limited by the available resources whether they are of type feedstock, consumable, or a tool. These agents and the groups into which they are organised answer to a set of norms and the culture in which they are established. Important to notice that STS is not simply a system involving the interaction between humans and technology; they rather endow technical interactions relying on specific and technical knowledge governing decision-making throughout and across the various levels of any organisational system.

In light of these concepts, we were able to describe a general conceptual class diagram to serve as the foundation for any STS regardless of their domain, and provided a STS Reference Architecture with different stakeholder involvement throughout its different layers. Our reference model is documented and also available in a technical paper to be published in the proceedings of

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Chapter 4

Results and Analysis

In this chapter, we describe the produced solution in4.1 NetLogo Implementation, as well as the simulation scenarios carried out upon the model in4.2Simulation Scenarios, and, finally, we analyse the results obtained from the performed simulations in4.3Assessment and Discussion.

4.1 NetLogo Implementation

The outbreak of the COVID-19 pandemic has recently called upon appropriate measures and put most cities to the test regarding their ability to adapt and overcome such an urgent crises.

With most health resources becoming rapidly over-saturated, such as hospitals, and in order to quickly respond to the crisis, Porto City Council relied on a group of professional volunteers to build and run a campaign hospital to add to its health system capacity. We use this scenario to illustrate the representational ability of our model. The implemented code can be found in appendixA.

4.1.1 ODD Protocol

The ODD protocol was chosen to document the model developed due to the growing understanding of its ability to standardize descriptions of ABMs so they can be easily reviewed, compared or replicated. The usefulness of this protocol is evident in Grimm(2008) and Grimm et al.(2010)’s research, which suggests ODD represents an intuitive (natural and logical) way of composing a model, facilitates analysis and comparison of ABMs, and ensures clearer communication between different disciplines and levels of organization. An additional advantage is the fact that ODD is independent from the programming language and environment. Although it is not particularly suitable for models developed using OOP, it is not the case for the platform used in this case, NetLogo [42][23]. However, in relation to Grimm et al.(2006)’s original version of the ODD protocol [22], Polhill et al. (2008) concluded that it was promising as a standard communication mechanism but in need of refinement to better accommodate the needs of ABMs as its original purpose was to document individual-agent models alone [42]. To that end, Grimm et al.(2010)

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24 Results and Analysis

proposed to review and update the protocol leading to a revised version with some alterations and additions[23].

The model description follows the revised version of the ODD protocol ([22], [23]). 1. Purpose

The model intends to simulate the complex STS of the “hospital de campanha” at “Pavilhão Rosa Mota” so as to test different operating policies to increase its efficiency.

2. Entities, State Variables, and Scales (see table4.1for state variables)

• Individuals: Patient, Doctor, Nurse, Auxiliary • Collectives: Patients, Doctors, Nurses, Auxiliaries • Environment: Beds

• Spatial Units: Cells

• Scales: One time step represents 1 day and simulations were run for 100 days. 3. Process Overview and Scheduling

• Patient: Occupy a bed; Become healed and free a bed; Wait in Waiting List; Recover according to the passage of time, own collaborativeness with the provided treatment, and the empathy inherent to the medical staff’s visits.

• Doctor/Nurse/Auxiliary: Visit assigned patients for that day the number of assigned times.

4. Design Concepts

• Objectives: Reduce patients’ recovery duration; Reduce patients’ time in waiting list (without treatment); Keep capacity < 80%; Reduce working hours for staff; Reduce resources spent.

• Interaction: Visits take an average of 15 minutes with additional 5/10/15 minutes if they are paid by empathetic medical staff; The average of visits must be >= 70% empathetic to positively affect recovery duration; The average of visits must present a >= 80% degree of collaborativeness to positively affect recovery duration; Number of assigned patients, visits, arriving patients, recovery duration, and collaborativeness are all determined through a normal distribution.

• Stochasticity: Additional 5/10/15 minutes for visits paid by empathetic medical staff and which patient is assigned to which medical staff are random;

• Collectives: Entity Patients establishes the group of all entities of type Patient, and so on for all other collectives.

• Observation: Average occupancy rate; Average time in the waiting list; Average daily visits duration; Average single visit duration.

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4.1 NetLogo Implementation 25

Table 4.1: State variables of ODD entities

Entity State Variables

Patient Healed: [0,1]

Collaborativeness: [0..100%] Recovery Duration: [int] Time in Waiting List: [int] Doctor; Nurse; Auxiliary Empathy: [0,1]

Patients #InBed: int > 0

#InWaitingList: int >= 0 #Healed: int >= 0

Doctors; Nurses; Auxiliaries #AvailableVolunteers: int > 0

Beds Free: [0,1]

5. Initialisation: Set up info (number of beds, doctors...).

6. Input Data: Number of beds and medical staff as in the actual hospital we modelled after. 7. Submodels: All values are configurable.

4.1.2 Interface

The implemented NetLogo interface is as shown in figure4.1with legend numbers corresponding to the following components:

1. Graphics window where there is visual representation of the beds, patients, and medical staff;

2. Settings where the user can choose the set up data;

• A chooser for the scenario (drop down with options 1, 2, and 3); • An input for the number of days the simulation should run for; • A slider for the number of beds;

• A slider for the number of doctors;

• A slider for the percentage of empathetic doctors; • A slider for the number of nurses;

• A slider for the percentage of empathetic nurses; • A slider for the number of health auxiliaries;

• A slider for the percentage of empathetic health auxiliaries; • A slider for the mean number of new patients per day; • A slider for the variance of new patients per day;

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26 Results and Analysis

Figure 4.1: Implemented NetLogo interface with legend numbers.

• A slider for the variance of days it takes a patient to recover. 3. Controls where the user can set up and run the simulation;

• Set Up, a button to initialise the system with the values chosen in the Settings; • Go, a button to perform all methods and procedures for a tick (one tick equals one

day). This button can be set to run until it reaches the number of days the simulation should run for the user wrote in the input in the Settings or it can be pressed by the user for every tick;

4. Views with three monitors which show the id of the patients in bed and in the waiting list at each tick, and the ids of all the healed patients;

5. Views with plots for the four main performance variables we intend to analyse and discuss further, average occupancy rate, average waiting time, average visits duration, and average single visit duration.

4.2 Simulation Scenarios

The developed model offers the possibility of three different scenarios each with increasing com-plexity concerning the existing agents, and their characteristics and behaviors so as to observe different emergent phenomena.

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4.2 Simulation Scenarios 27

4.2.1 Scenario 1

This scenario relies on patients alone without the intervention of any medical staff.

The hospital is set up with a certain number of beds evenly distributed through space and patients are only allowed in according to the number of beds available each day. Both the arrival rate and recovery duration rate for patients are determined through a normal distribution. The patients fill the beds according to whom arrives first and, if there aren’t any available beds, they are put into a waiting list until a new bed is made available, and they can be admitted into it. Everyday, a day is taken from the recovery duration of the patients in bed and once it reaches the value of zero days, those patients can free their beds and are considered to be healed.

4.2.1.1 System performance variables

Since this scenario takes one day from the total recovery duration every day without exception for all patients in bed, the system performance variables rely solely on the number of patients and their recovery duration. Therefore, we consider:

• Average occupancy rate which returns the average number of beds occupied every day; • Average time in the waiting list which returns the average number of days spent by patients

waiting for a bed to vacate.

4.2.1.2 Run Simulation

For this scenario, we ran two different simulations considering a worst and average cases.

As for all other scenarios and simulations, the number of beds was chosen according to the actual conditions of the hospital we modelled after, amounting to a total of 320 beds.

The mean number of patients was chosen based on data retrieved from DGS, the public body of the Ministry of Health in Portugal [1]. According to the information provided to the public, there has been a total of 63,310 cases of Covid-19, for a period of 195 days (63, 310/195 = 324.(6) infected patients/day). Considering 36% of the total cases in the country are from the North, for the average case, we considered an average of 110 new infected patients per day (324.(6) ∗ 0.36 = 109.088 infected patients/day in the North) with a 10 variance, and 15 days for the mean recovery duration (approximately two weeks also in line with the available information released to the public) with a variance of 5 days. For the worst case, we considered all the same values with the exception of the number of new patients per day. The worst day in terms of new infected patients in Portugal had statistics of 1,516 new cases. Considering the same 36% with respect to the North, we arrive at a mean of 545 new patients per day (1, 516 ∗ 0.36 = 545.76). This set up info is summed up in table4.2.