Causal Mapping Research Articles

Enhancing systemic thinking by sharing experiences of reading literary fiction using causal mapping

Great works of literary fiction seem to mirror life and its complexity. This paper claims that engaging in reading fiction can be beneficial for OR practitioners as it enhances systems thinking and understanding of complex human affairs. Using the experience of reading a fiction book, we applied Causal Mapping (CM) to Reading Labs, where participants read and share their views of fiction literature to appreciate the complexity of multiple and differing reading perspectives. The approach we adopt hinges on the relationship between literature and OR, two disciplines which belong to different fields of knowledge (humanities and science, respectively), but when examined in more detail, connect in meaningful ways. We explore this connection to identify potential gains of increasing systemic thinking awareness in the reading groups’ context. The findings of this interdisciplinary study show that use of CM (i) enhances systemic thinking by producing a synthesis and shared views on what was meaningful and useful; and (ii) translates the subjectivity produced by the shared reading experience into new actions strengthened by systemic thinking awareness. These results should be of interest to Soft/OR practitioners using CM and systems practitioners working on encouraging the use of systems thinking in systemic interventions.

Implementing sustainable innovation in state universities: Process and tools

Universities have the vision, the knowledge and the power to lead the change towards a more sustainable world. However, there is a lack of studies investigating how universities can systematically reorient themselves to help society become more sustainable. In general, a research gap exists in the implementation of the business model innovation process, in the tools that should be used to pass from the design stage to implementation and in the identification of the main challenges that may hinder this process. This paper adopts the concept of a sustainable business model for interpreting the core logic through which universities create value for themselves, society and the environment. In the first stage of research, a theoretical framework was proposed for diagnosis of the integration of sustainability in the business models of universities. This framework was applied in the context of Italian state universities to identify business models with different degrees of strategic orientation towards sustainability. Now the research aim is to examine the processes and tools used and challenges faced by Italian state universities when implementing sustainable innovation. A multiple-case study is carried out on four Italian state universities showing a high level of integration of sustainability in all elements of the business model. The combination of both quantitative and qualitative data, and the patterns observed across cases, led to the presentation of five theoretical propositions, which were summarized in a preliminary model of the processes, tools and challenges involved when implementing sustainable innovation in higher education. The main theoretical contributions of this study are the advancement of knowledge regarding the dynamics (process) and the elements (tools) for the implementation of sustainability strategies in state universities, and the provision of a new causal map of the relationships among the variables that affect sustainable innovation in higher education.

SAPEVO-H² a Multi-Criteria Systematic Based on a Hierarchical Structure: Decision-Making Analysis for Assessing Anti-RPAS Strategies in Sensing Environments

Regarding high-level and complex decision-making scenarios, the study presents an extensive approach to the Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi Decision Making method (SAPEVO-M). In this context, the modeling proposal, named SAPEVO-Hybrid and Hierarchical (SAPEVO-H²), the objective of this study, based on the concepts of multi-criteria analysis, provides the evaluation of alternatives under the light of multiple criteria and perceptions, enabling the integration of the objectives of a problem, which are transcribed into attributes and structured in a hierarchical model, analyzing qualitative and quantitative data through ordinal and cardinal entries, respectively. As a case study, a decision analysis concerning the defense strategies against anti-Remotely Piloted Aircraft Systems (RPAS) strategies for the Brazilian Navy is carried out. Using the technique of the causal maps approach based on Strategic Options Development and Analysis (SODA) methodology, the problematic situation is structured for numerical implementation, demonstrating the performance of objectives and elements of a hierarchical structure. As a result, rankings concerning objectives and anti-RPAS technologies, based on the treatment of subjective information, are presented. In the end, the main contribution of the study and its limitations are discussed, along with the conclusions and some proposals for future studies.

Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neurodevelopment

BackgroundThe combined effects of multiple environmental toxicants and social stressor exposures are widely recognized as important public health problems, likely contributing to health inequities. However, US policy makers at state and federal levels typically focus on one stressor exposure at a time and have failed to develop comprehensive strategies to reduce multiple co-occurring exposures, mitigate cumulative risks and prevent harm. This research aimed to move from considering disparate environmental stressors in isolation to mapping the links between environmental, economic, social and health outcomes as a dynamic complex system using children's exposure to neurodevelopmental toxicants as an illustrative example. Such a model can be used to support a broad range of child developmental and environmental health policy stakeholders in improving their understanding of cumulative effects of multiple chemical, physical, biological and social environmental stressors as a complex system through a collaborative learning process. MethodsWe used system dynamics (SD) group model building to develop a qualitative causal theory linking multiple interacting streams of social stressors and environmental neurotoxicants impacting children's neurodevelopment. A 2 1/2-day interactive system dynamics workshop involving experts across multiple disciplines was convened to develop the model followed by qualitative survey on system insights. ResultsThe SD causal map covered seven interconnected themes: environmental exposures, social environment, health status, education, employment, housing and advocacy. Potential high leverage intervention points for reducing disparities in children's cumulative neurotoxicant exposures and effects were identified. Workshop participants developed deeper level of understanding about the complexity of cumulative environmental health risks, increased their agreement about underlying causes, and enhanced their capabilities for integrating diverse forms of knowledge about the complex multi-level problem of cumulative chemical and non-chemical exposures. ConclusionGroup model building using SD can lead to important insights to into the sociological, policy, and institutional mechanisms through which disparities in cumulative impacts are transmitted, resisted, and understood.

Causal mapping of human brain function to identify TMS targets

Causal mapping of human brain function to identify TMS targets

To Speak up or Shut up? Revealing the Drivers of Crowdworker Voice Behaviors in Crowdsourcing Work Environments

This study examines worker voice behaviors in the microtask crowdsourcing work environment (CSWE) where voice channels are absent. Informed by employee voice research, this study adopts the revealed causal mapping method to analyze the detailed narratives of 60 workers from Amazon Mechanical Turk. Our data analysis shows that the crowdworkers did engage in voice behaviors, but their voices were not always heard, depending on recipients. The crowdworker voice was directed to three different recipients (worker community, job requester, and platform) and influenced by six antecedents (duty orientation, efficacy judgment, workgroup identification, anger/frustration, futility, and achievement orientation). Based on the findings, we propose a model of worker voice antecedents and moderators in the CSWE. This study extends employee voice research by presenting a moderator perspective in the CSWE. Moreover, our study provides a nuanced understanding of crowdworker voice behaviors from two major aspects – antecedent and recipient – contributing to crowdsourcing research.

Holography of geodesic flows, harmonizing metrics, and billiards' dynamics

For a traversing vector field $v$ on a compact $(n+1)$-manifold $X$ with boundary, we use closed $v$-invariant differential $n$-forms $\Theta$ to define measures $\mu_\Theta$ on the boundary $\partial X$, such that the $v$-flow generated causality map $C_v: \partial^+X(v) \to \partial^-X(v)$ preserves $\mu_\Theta$. In combination with a $\mu_\Theta$-preserving involution $\tau: \partial X \to \partial X$, which maps $\partial^-X(v)$ to $\partial^+X(v)$, the proto-billiard map $B_{v, \tau} := \tau \circ C_v$ is a measure-preserving transformation of $\partial^+X(v)$. We study the dynamical system $(\partial^+X(v), B_{v, \tau})$. Let $(M, g)$ be a Riemannian $n$-manifold with boundary, where $g$ is a non-trapping metric. Let $SM$ be the space of the spherical tangent to $M$ bundle, and $v^g$ the geodesic vector field on $SM$. We apply our treatment of $C_v$ and $(\partial^+X(v), B_{v, \tau})$ to the scattering maps $C_{v^g}$ and billiard maps $B_{v^g, \tau}$, where $\tau$ is generated by the reflection of unit tangent vectors with respect to $\partial M$. As a result, we getting a variety of holography theorems that tackle the inverse scattering problems for $C_{v^g}$ and theorems that describe the dynamics of $B_{v^g, \tau}$. Our main tool is the Lyapunov function $F: SM \to \mathbf R$ for $v^g$ and the metrics $g^\bullet$ on $SM$ that make $dF$ harmonic. For such metrics, we get isoperimetric inequalities $vol_{g^\bullet}(SM) \leq vol_{g^\bullet |}(\partial(SM))$. Assuming ergodicity of $B_{v^g, \tau}$ and non-trapping of $g$, we also get formulas for the average length of free geodesic segments in $M$.

Spatiotemporal Learning via Mixture Importance Gaussian Filtering With Sparse Regularization

This letter proposes a novel spatiotemporal learning model via mixture importance Gaussian filtering (MIGF). In the MIGF, we explore the causal mapping between the target's true parameters and the latest measurement, and present an approach for elegantly combining the deterministic Gaussian-Hermite integral with the stochastic importance sampling method. This formulation allows for the use of soft-constrained and sparse regularization to reduce the truncation error and improve the weight adaptivity. We also provide an effective causal invariant updating rule to learn the parameters of this constrained dynamic model with a convergence guarantee. Experimental results show that MIGF can effectively eliminate the constraint uncertainty for the constrained dynamics.

시스템사고로 본 개성공단 기업의 경영성과 인과순환구조

The purpose of this study is to examine the structure of the Kaesong Industrial Complex companies through which mechanisms were able to create business performance. The Kaesong Industrial Complex was the only case in the world where two disparate economic systems, the socialist reform economy and the capitalist market economy, cooperated. The total output of the Kaesong Industrial Complex increased steadily and showed good business performance, but some companies did not. As a result of confirming the difference in management performance between companies through the causal cycle map, the management capacity of the company made the difference. Therefore, in order to prepare for the resumption of inter-Korean economic cooperation, it is necessary to continually provide education and consulting for companies.

Root cause diagnosis for complex industrial process faults via spatiotemporal coalescent based time series prediction and optimized Granger causality

It is essential to conduct root cause diagnosis (RCD) to guarantee the safety of operations of chemical processes and suppress fault deterioration, yet RCD has encountered challenges in the context of the increasing complexity of industrial processes. Granger causality (GC) analysis is one of the most commonly used methods to construct process causal maps and identify root causes. However, its use is subject to a number of limitations for dynamic nonlinear industrial processes. Therefore, an optimized GC analysis is proposed to perform RCD in complex industrial processes based on the spatiotemporal coalescent-based prediction model (SCPM) and causality verification algorithm (CVA). The time series prediction model SCPM is presented as an alternative to GC’s conventional autoregressive model in order to avoid spurious regressive facing nonlinear processes, which stacks dilated convolutional neural networks (DCNN) and bidirectional gated recurrent unit (BiGRU) to coalesce time series nonlinear couplings and long-term dependence information. Moreover, considering data that may deviate from normal distribution after the fault occurs, a nonparametric causality test method CVA based on the Wilcoxon signed-rank test (WSRT) is constructed combined with SCPM to eliminate false causality discovery. Empirical results on the Tennessee Eastman process and the blast furnace process demonstrate the effectiveness of the proposed RCD method.

Different Features of a Metabolic Connectivity Map and the Granger Causality Method in Revealing Directed Dopamine Pathways: A Study Based on Integrated PET/MR Imaging.

Exploring the directionality of neural information in the brain is important for understanding brain mechanisms and neurodisease development. Granger causality analysis and the metabolic connectivity map can be used to investigate directional transmission of information between brain regions, but their differences in depicting functional effective connectivity are not clear. Using the Monash rs-PET/MR imaging data set, we conducted Granger causality and metabolic connectivity map analyses of the dopamine reward circuit in the brain. The dopamine reward circuit is a well-known system consisting primarily of the bilateral orbital frontal cortex, caudate, nucleus accumbens, thalamus, and substantia nigra. We validated these circuit pathways using Granger causality and the metabolic connectivity map for identifying effective connectivities against a priori knowledge by testing the significance of directed pathways (P < .05, false discovery rate-corrected). We found 3 types of effective connectivities in the dopamine reward circuit: long-range, neighborhood, and symmetric. Granger causality analysis revealed long-range connections in the orbital frontal cortex-caudate and orbital frontal cortex-nucleus accumbens regions. Metabolic connectivity map analysis revealed neighborhood connections in the nucleus accumbens-caudate, substantia nigra-thalamus, and thalamus-caudate regions. Metabolic connectivity map analysis also found symmetric connections in each of the bilateral nucleus accumbens, caudate, thalamus, and orbital frontal cortex-caudate regions. Different patterns in directional networks of the dopamine reward circuit were revealed by Granger causality and metabolic connectivity map analyses. Granger causality analysis primarily identified bidirectional cortico-nucleus connections, while the metabolic connectivity map primarily identified direct connections among neighborhood and symmetric regions. The results of this study indicated that investigations of effective connectivities should use an appropriate analysis method depending on the purpose of the study.

The role of forensic investigation in systemic risk enquiry: Reflections from case studies of disasters in Istanbul, Kathmandu, Nairobi, and Quito

The systemic nature of risk is increasingly acknowledged within scholarship, policy and practice relating to disaster management. However, a number of conceptual and methodological challenges arise in advancing empirical inquiry in this regard. These challenges relate to how the boundaries of the system are determined both spatially and temporally, how expertise from across disciplines is integrated to allow for consideration of institutional and broad socio-economic drivers of risk in addition to physical drivers, and, crucially, how causality operates within system complexity. The potential of forensic investigations of disasters that typically deploy in-depth case studies to overcome these obstacles is evaluated on the basis of causal mapping with experts from a range of disciplinary backgrounds in Istanbul, Kathmandu, Nairobi and Quito. It is found that such investigations can serve to interrogate the fundamental value of any given system and its spatial and temporal bounds, generate collective mental models of the system from which risk emerges, and drive reflection on its root causes. However, it is critical that forensic investigation approaches carefully consider participant selection and facilitation in order to effectively operationalise the systemic risk concept in complementarity with other approaches.

An Exploratory Study on Communication Complexity in Mega Construction Projects

Complexity is the property of a project that makes it difficult to understand, foresee and control its behaviour due to interrelatedness between various project attributes such as tasks, parties, disciplines, and interfaces. Project complexity is an inherent part of mega construction projects due to their large scale, long period, the multiplicity of disciplines, the high number of stakeholders, multi-nationality, and high level of public attention, as well as political interest. This paper depicts findings of a research project that aims to identify characteristics of and factors that contribute to project complexity by referring to the experiences of professionals involved in mega construction projects. For this purpose, data has been collected by interviewing 18 participants from 11 mega construction projects. Communication complexity, which reflects the amount of distributed communication channels between various stakeholders, has been identified as one of the major drivers of project complexity in megaprojects. Based on literature and semi-structured interview findings, a causal structure to represent communication complexity comprising of three components, which are; project, stakeholders, and communication management, was developed. The causal map, which represents the opinions of a limited number of experts, cannot be generalised; however, it can provide useful insights about drivers of communication complexity that can be used to develop strategies to manage complexity.

An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions

An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions, and initialized by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> domain knowledge and preprocessed public datasets. The system defines a causal and temporal mapping of worker and machine states where one side can label the other automatically. A case study in machines with different automation levels is conducted in a multiuser facility by using finite state machine representations for machine energy states, worker activity states, and interaction transition functions. The fully automated adaptive ML system improves its accuracy for human machine interaction detection by up to 12.5% and shows potential to recognize more fine-grained actions.

Using Strategic Options Development and Analysis (SODA) to understand the simulation accessibility problem

Simulation modelling is applied to a wide range of problems, including defence and healthcare. However, there is a concern within the simulation community that there is a limited use and implementation of simulation studies in practice. This suggests that despite its benefits, simulation may not be reaching its potential in making a real-world impact. The main reason for this could be that simulation tools are not widely accessible in industry. In this paper, we investigate the issues that affect simulation modelling accessibility through a workshop with simulation practitioners. We use Strategic Options Development and Analysis (SODA), a problem-structuring approach that allows for the stakeholder views to be expressed and linked in a systematic way. The causal map derived represents the emerging concepts and their effects, with the view to identifying their impact on the accessibility problem. We present our analysis of the issues and options identified. Based on our findings, we discuss the implications and recommendations for the future uptake of simulation.

Towards Developing Metrics to Evaluate Digital Engineering

AbstractModel‐based systems engineering (MBSE) is an increasingly accepted practice in the Systems Engineering (SE) community, however, little has been done to empirically show that MBSE provides value. Furthermore, as the industry continues in the direction of digital transformation, MBSE will become a critical component of the larger Digital Engineering (DE) approach. This paper presents a measurement framework for selecting and developing appropriate metrics to assess the value/benefits of MBSE and subsequently DE. Utilizing expected benefits identified in a review of MBSE literature, a causal map was hypothesized to show how expected benefits (potential metrics) influence and relate to each other. This was done in order to systematically determine which benefits would be the most impactful to measure. The hypothesized causal model was presented for feedback to subject‐matter experts from a working group developing the first DE measurement framework. This group is a joint effort with industry, academia, and the USA government to develop DE metric standards. Once the causal map was finalized, a case study was used to partially validate the causal model. Based on the causal map and subsequent analysis, we can recommend the first metrics to be employed for DE/MBSE based on the most influential nodes of the causal model. The potential metric candidates include: system quality, defects, time, rework, ease of making changes, system understanding, Effort, accessibility of information, collaboration, project methods/processes, and use of DE/MBSE tools. We believe a concerted effort across the industry to focus on measuring these variables is the most effective way to establish proof of the value of MBSE and DE.

Stable shredded spheres and causal random maps with large faces

We introduce a new familiy of random compact metric spaces Sα for α∈(1,2), which we call stable shredded spheres. They are constructed from excursions of α-stable Lévy processes on [0,1] possessing no negative jumps. Informally, viewing the graph of the Lévy excursion in the plane, each jump of the process is “cut open” and replaced by a circle, and then all points on the graph at equal height, which are not separated by a jump, are identified. We show that the shredded spheres arise as scaling limits of models of causal random planar maps with large faces introduced by Di Francesco and Guitter. We also establish that their Hausdorff dimension is almost surely equal to α. Point identification in the shredded spheres is intimately connected to the presence of decrease points in stable spectrally positive Lévy processes, as studied by Bertoin in the 1990s.

시스템다이내믹스를 활용한 독서문화진흥정책의 인과지도 분석에 관한 연구

With the enactment of the 「Reading Culture Promotion Act」, various reading policies were prepared such as the establishment of a reading culture foundation, the establishment of reading infrastructure, and the expansion of the reading population. In accordance with the law, the government established the ‘Basic Plan for the Promotion of Reading Culture’ and promoted the policy, and invested a lot of time and policy money to promote reading. However, the ecosystem of the reading culture promotion policy is evaluated as not having self-sufficiency as the amount of reading is decreasing. Therefore, this study explored policy variables based on system thinking and analyzed the causal map between policy variables to activate the reading culture promotion policy ecosystem. System dynamics was used for analysis. Twenty policy variables were selected through literature and policy reports. Based on these policy variables, the reading culture promotion policy was analyzed through system dynamics through causal guidance and feedback loop. As a result of the analysis, 8 feedback loops appeared. The feedback loop includes establishment of a reading welfare system, expansion of reading movement, establishment of digital reading content, establishment of a cooperative system for reading policy, nurturing of reading manpower, expansion of the foundation for reading culture, expansion of publication and distribution of excellent books, and the government's reading support policy.

A systems approach to mapping UK regional innovation ecosystems for policy insight

The literature on innovation and regional economic development shows widespread references to ‘systems’ but relatively little application of systems thinking and problem structuring methods. Innovation models are thus under-theorised with operationally-based maps that link the structure of innovation systems to behaviour and possible policy levers. This conceptual paper addresses this shortfall by applying a systems mapping approach to regional innovation ecosystems which sets out the structural drivers and actor inter-relationships in regional economies with universities acting as focal points for knowledge application. A causal feedback map is hypothesised from participative research and the innovation literature, providing an integrative framework and enabling initial policy discussions about the drivers of regional innovation. Hence this model identifies the virtuous reinforcing behaviours that act endogenously within an innovation ecosystem to drive economic growth. Several policy questions concerning regional innovation and university– industry–government collaboration are surfaced by the model. Finally, possible policy interventions are proposed.

Quality-Discriminative Localization of Multisensor Signals for Root Cause Analysis

Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.