Integrating Computational and Participatory Simulations for Design in Complex Systems

The understanding and conceptualization of cities and its constituent systems such as transportation and healthcare as open and complex is shifting the debates around the technical and communicative rationales of planning. Viewing cities in a holistic manner presents methodological challenges, where our understanding of complexity is applied in a tangible fashion to planning processes. Bridging the two rationales in the tools and methodologies of planning is necessary for the emergence of a 'non-linear rationality' of planning, one that accounts for and is premised upon complexity. Simulations representing complex systems provide evidence and support for planning, and have the potential to serve as an interface between the more abstract and political decision making and the material city systems. Moving beyond current planning methods, this thesis explores the role of simulations in planning. Recognizing the need for holistic representations, the thesis integrates multiple disparate simulations into a holistic whole achieving complex representations of systems. These representations are then applied and studied in an interactive environment to address planning problems in different contexts. The thesis contributes an approach towards the development of complex representations of systems; improvements on participatory methods to integrate computational simulations; a nuanced understanding of the relative value of simulation constructs; technologies and frameworks that facilitate the easy development of integrated simulations that can support participatory planning processes. The thesis develops contributions through experiments which involved problems and stakeholders from real world systems. The approach towards development of integrated simulations is realized in an open source framework. The framework creates computationally efficient, scalable and interactive simulations of complex systems, which used in a participatory manner delivers tangible plans and designs.QC 20170602</p

DISENTANGLING THE COMPLEXITY OF INDIA’S AGRICULTURAL SECTOR

Agricultural policies in India directly impact the livelihoods of close to two thirds of India’s population. Through policies, the government manages food security, urban and rural poverty, energy, and infrastructure, among others. Given the current state of India’s governance, the connection between policy making and its results in society becomes a key issue for research. This paper presents a game for use as a research instrument. The game can facilitate research into the policy making process at various levels of the government in India. The design is intended to understand the complexity of the institutional arrangement that defines and implements agricultural policies. The game integrates with other games that simulate other aspects of the agricultural system in India. The paper presents the verification and validation cycles followed, and identifies further steps for field validation

Disentangling the complexity of India ’s agricultural sector

Agricultural policies in India directly impact the livelihoods of close to two thirds of India’s population. Through policies, the government manages food security, urban and rural poverty, energy, and infrastructure, among others. Given the current state of India’s governance, the connection between policy making and its results in society becomes a key issue for research. This paper presents a game for use as a research instrument. The game can facilitate research into the policy making process at various levels of the government in India. The design is intended to understand the complexity of the institutional arrangement that defines and implements agricultural policies. The game integrates with other games that simulate other aspects of the agricultural system in India. The paper presents the verification and validation cycles followed, and identifies further steps for field validation

Curating Player Experience Through Simulations in City Games

The use of games as a method for planning and designing cities is often associated with visualisation, from simplistic to immersive environments. They can also include complex and sophisticated models which provide an evidence base. The use of such technology as artefacts, aids, or mechanics curates the player experience in different and very often subtle ways, influencing how we engage with (simulated) urban phenomena, and, therefore, how the games can be used. In this article, we aim to explore how different aspects of technology use in city games influence the player experience and game outcomes. The article describes two games built upon the same city gaming framework, played with professionals in Rome and Haifa, respectively. Using a mixed-method, action research approach, the article examines how the high-tech, free form single-player games elicit the mental models of players (traffic controllers and planners in both cases). Questionnaires and the players’ reflections on the gameplay, models used, and outcomes have been transcribed and analysed. Observations and results point to several dimensions that are critical to the outcomes of digital city games. Agency, exploration, openness, complexity, and learning are aspects that are strongly influenced by technology and models, and in turn, determine the outcomes of the game. City games that balance these aspects unlock player expertise to better understand the game dynamics and enable their imagination to better negotiate and resolve conflicts in design and planning

Tensions between real-world practices and the digitalization paradigm for data-driven services in eldercare: observations from an ethnographic study in Sweden

Abstract Background The implementation of a data-driven approach within the health care system happens in a rapid pace; including in the eldercare sector. Within Swedish eldercare, data-driven health approach is not yet widely implemented. In the specific context of long-term care for older adults, quality of care is as much determined by how social care is being performed as it is by what kind medical care that is provided. In particular, relational aspects have been proven to have a crucial influence on the experience of quality of care for the actors involved. Drawing on ethnographic material collected at a Swedish nursing home, this paper explores in what way the relational aspects of care could potentially become affected by the increased use of a data-driven health approach. Methods An ethnographic approach was adopted in order to investigate the daily care work at a long-term care facility as it unfolded. Fieldwork was conducted at a somatic ward in a Swedish long-term care facility over 4 months (86 h in total), utilizing the methods of participant observation, informal interviews and document analysis. The material was analyzed iteratively throughout the entire research process adopting thematic analysis. Results Viewing our ethnographic material through an observational lense problematising the policy discourse around data-driven health approach, two propositions were developed. First, we propose that relational knowledge risk becoming less influential in shaping everyday care, when moving to a data-driven health approach. Second, we propose that quality of care risk becoming more directed on quality of medical care at the expense of quality of life. Conclusion While the implementation of data-driven health approach within long-term care for older adults is not yet widespread, the general development within health care points towards a situation in which this will become reality. Our study highlights the importance of taking the relational aspects of care into consideration, both during the planning and implementation phase of this process. By doing this, the introduction of a data-driven health approach could serve to heighten the quality of care in a way which supports both quality of medical care and quality of life

A Feasibility Study for Gamification in Transport Maintenance : Requirements to implement gamification in heterogeneous organizations

Gamification has been successfully applied in many domains, but mostly for simple, isolated and operational tasks. The hope for gamification as a method to radically change and improve behavior, to provide incentives for sustained engagement has proven to be more difficult to get right. Applying gamification in large networked organizations with heterogeneous tasks remains a challenge. Applying gamification in such enterprise environments posits different requirements, and a match between these requirements and the institution needs to be investigated before venturing into the design and implementation of gamification. The current paper contributes a study where the authors investigate the feasibility of implementing gamification in Trafikverket, the Swedish transport administration. Through an investigation of the institutional arrangements around data collection, procurement processes and links to institutional structures, the study finds areas within Trafikverket where gamification could be successfully applied, and suggests gaps and methods to apply gamification in other areas.QC 20160215</p

Technology Adoption in Air Traffic Management: A Combination of Agent-Based Modeling with Behavioral Economics

The European Air Traffic Management (ATM) system is responsible for the safe and timely transportation of more than a billion passengers annually. It is a system that depends heavily on technology and is expected to stay on top of the technological advancements and be an early adopter of technologies. Nevertheless, technological change in ATM has historically developed at a slow pace. In this paper, an agent-based model (ABM) of the ATM technology deployment cycle is proposed. The proposed ABM is part of a larger project, which intends to recommend new policy measures for overcoming any barriers associated with technology adoption in ATM. It is a novel and one of the first approaches aiming at simulating the adoption of technology in ATM that combines the organizational point of view, i.e. stakeholders’ level, the focus on policy testing and the inclusion of behavioral economics aspects.QC 20210618</p

Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.QC 20230628</p

A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.QC 20221115</p