What We Can Learn about Business Modeling from Homeostasis

Business modeling methods most often model an organization’s value provision to its customers followed by the necessary activities and structure to deliver this value. These activities and structure are seen as infinitely malleable; they can be specified and engineered at will. This is hardly in line with what even laymen can observe of organizations, that they are not easy to change and that their behavior often is not directly centered on providing value to customers. Homeostasis is an almost century old model that was developed in the field of physiology to explain how living beings survive by maintaining the constancy of their internal states. Homeostasis helps to explain both the inability of organizations to provide maximum value to their customers and their reluctance to change. From this point of view, resistance to change is not something to fight or to ignore but an essential force behind organizational behavior that can either enable or defeat new business models

The Virtual Runner Learning Game

A learning game has been developed which allows learners to study and learn about the significance of three important variables in human physiology (lactate, glycogen, and hydration) and their influence on sports performance during running. The player can control the speed of the runner, and as a consequence the resulting physiological processes are simulated in real-time. The performance degradation of the runner due to these processes requires that different strategies for pacing the running speed are applied by the player, depending on the total length of the run. The game has been positively evaluated in a real learning context of academic physiology teaching

Simulation modelling: Educational development roles for learning technologists

Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, ‘adaptive and productive’ media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies ‐ one that realizes their creative‐transformative potential

The role of Computer Aided Process Engineering in physiology and clinical medicine

This paper discusses the potential role for Computer Aided Process Engineering (CAPE) in developing engineering analysis and design approaches to biological systems across multiple levels—cell signalling networks, gene, protein and metabolic networks, cellular systems, through to physiological systems. The 21st Century challenge in the Life Sciences is to bring together widely dispersed models and knowledge in order to enable a system-wide understanding of these complex systems. This systems level understanding should have broad clinical benefits. Computer Aided Process Engineering can bring systems approaches to (i) improving understanding of these complex chemical and physical (particularly molecular transport in complex flow regimes) interactions at multiple scales in living systems, (ii) analysis of these models to help to identify critical missing information and to explore the consequences on major output variables resulting from disturbances to the system, and (iii) ‘design’ potential interventions in in vivo systems which can have significant beneficial, or potentially harmful, effects which need to be understood. This paper develops these three themes drawing on recent projects at UCL. The first project has modeled the effects of blood flow on endothelial cells lining arteries, taking into account cell shape change resulting in changes in the cell skeleton which cause consequent chemical changes. A second is a project which is building an in silico model of the human liver, tieing together models from the molecular level to the liver. The composite model models glucose regulation in the liver and associated organs. Both projects involve molecular transport, chemical reactions, and complex multiscale systems, tackled by approaches from CAPE. Chemical Engineers solve multiple scale problems in manufacturing processes – from molecular scale through unit operations scale to plant-wide and enterprise wide systems – so have an appropriate skill set for tackling problems in physiology and clinical medicine, in collaboration with life and clinical scientists

The cybernetic Bayesian brain: from interoceptive inference to sensorimotor contingencies

Is there a single principle by which neural operations can account for perception, cognition, action, and even consciousness? A strong candidate is now taking shape in the form of “predictive processing”. On this theory, brains engage in predictive inference on the causes of sensory inputs by continuous minimization of prediction errors or informational “free energy”. Predictive processing can account, supposedly, not only for perception, but also for action and for the essential contribution of the body and environment in structuring sensorimotor interactions. In this paper I draw together some recent developments within predictive processing that involve predictive modelling of internal physiological states (interoceptive inference), and integration with “enactive” and “embodied” approaches to cognitive science (predictive perception of sensorimotor contingencies). The upshot is a development of predictive processing that originates, not in Helmholtzian perception-as-inference, but rather in 20th-century cybernetic principles that emphasized homeostasis and predictive control. This way of thinking leads to (i) a new view of emotion as active interoceptive inference; (ii) a common predictive framework linking experiences of body ownership, emotion, and exteroceptive perception; (iii) distinct interpretations of active inference as involving disruptive and disambiguatory—not just confirmatory—actions to test perceptual hypotheses; (iv) a neurocognitive operationalization of the “mastery of sensorimotor contingencies” (where sensorimotor contingencies reflect the rules governing sensory changes produced by various actions); and (v) an account of the sense of subjective reality of perceptual contents (“perceptual presence”) in terms of the extent to which predictive models encode potential sensorimotor relations (this being “counterfactual richness”). This is rich and varied territory, and surveying its landmarks emphasizes the need for experimental tests of its key contributions

Psychophysical identity and free energy

An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind-brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.Comment: 22 pages; published as a research article on 8/5/2020 in Journal of the Royal Society Interfac

The nature of risk in complex projects

© 2017 Project Management Institute, Inc. Risk analysis is important for complex projects; however, systemicity makes evaluating risk in real projects difficult. Looking at the causal structure of risks is a start, but causal chains need to include management actions, the motivations of project actors, and sociopolitical project complexities as well as intra-connectedness and feedback. Common practice based upon decomposition-type methods is often shown to point to the wrong risks. A complexity structure is used to identify systemicity and draws lessons about key risks. We describe how to analyze the systemic nature of risk and how the contractor and client can understand the ramifications of their actions

The nature of risk in complex projects

© 2017 Project Management Institute, Inc. Risk analysis is important for complex projects; however, systemicity makes evaluating risk in real projects difficult. Looking at the causal structure of risks is a start, but causal chains need to include management actions, the motivations of project actors, and sociopolitical project complexities as well as intra-connectedness and feedback. Common practice based upon decomposition-type methods is often shown to point to the wrong risks. A complexity structure is used to identify systemicity and draws lessons about key risks. We describe how to analyze the systemic nature of risk and how the contractor and client can understand the ramifications of their actions