George Mobus' Selected Publications

Mobus, G.E., (2018). "Teaching systems thinking to general education students", Ecological Modelling, Elsevier, vol. 373(C), pages 13-21.

Based on my experiences teaching systems science to general education (not necessarily science) undergraduates, this paper appeared in a special issue of Ecological Modelling. I used a hands-on (problem/project-based) pedagogical style wherein the students, working in teams of 3-4 analyzed an agricultural system, based on a permaculture theme, and built a model of the system. They were tasked with answering a question: “How much land area would be needed to supply a vegan community of 500 people with all of their caloric needs for one year on farm near Seattle WA.?”

Mobus, G.E. (2017). "A Framework for Understanding and Achieving Sustainability of Complex Systems", Systems Research and Behavioral Science, 34:5, John Wiley & Sons, Ltd, doi: 10.1002/sres.2482. Online: available here Accessed 2/25/2019.

This journal article extends and expands my work on hierarchical cybernetic systems (governance) and how these are needed to achieve a sustainable society or any complex, adaptive, and evolvable system.

Mobus, G.E. & Anderson, K. (2016). "System Language: Understanding Systems", Proceedings of the 60th Annual Meeting of the ISSS - 2016, Boulder, Colorado.

Kevin Anderson and I worked on this early definition of a more comprehensive modeling language for describing and simulating complex, adaptive, and evolvable systems. We introduced a formalism for defining a system using a standard lexicon and a semantics based on the concept of systemness, which Mike Kalton and I developed in our textbook.

Mobus, G.E. (2015). "A Systems Science Framework for Understanding the Nature of Governance", Proceedings of the 59th Annual Meeting of the ISSS - 2016, Berlin, Germany. Online: available here

In this early paper I introduce the concept of a hierarchical cybernetic governance system (HCGS) comprised of three basic layers of decision types and temporal domains. Operations level governance is the typical feedback and some feedforward cybernetic mechanisms for establishing real-time operations of any complex physical plant. This is the level in which the work of the entity gets done and relies on local management decisions and cooperation communications. The next layer up is the coordination layer in which longer-term decisions of logistics (internal) and tactical (external) coordination is achieved. Finally, for complex, adaptive, and evolvable systems such as organizations, and human beings, the highest and longest-term layer involves strategic management. The paper explains how this architecture is not necessarily a top-down command-and-control, nor is it particularly bureaucratic except in the cases where ordinary human beings are filling the roles of decision agents.

• Serves as a textbook for teaching systems fundamentals in any discipline or for use in an introductory course in systems science degree programs
• Addresses a wide range of audiences with different levels of mathematical sophistication
• Includes open-ended questions in special boxes intended to stimulate integrated thinking and class discussion
• Describes numerous examples of systems in science and society
• Captures the trend towards interdisciplinary research and problem solving

     This pioneering text provides a comprehensive introduction to systems structure, function, and modeling as applied in all fields of science and engineering. Systems understanding is increasingly recognized as a key to a more holistic education and greater problem solving skills, and is also reflected in the trend toward interdisciplinary approaches to research on complex phenomena. While the concepts and components of systems science will continue to be distributed throughout the various disciplines, undergraduate degree programs in systems science are also being developed, including at the authors’ own institutions. However, the subject is approached, systems science as a basis for understanding the components and drivers of phenomena at all scales should be viewed with the same importance as a traditional liberal arts education.

Mobus, G.E. (2012). “The Evolution of Wisdom”, Science, Wisdom, and the Future: Humanity's Quest for a Flourishing Earth, Collins Foundation Press, Santa Margarita, CA. pp 83-89.

Introduction: In this chapter I explore why intelligence and creativity, normally associated with wisdom, are not sufficient for the purposes of scientific study. I further propose to provide a general concept of sapience that allows us to consider directly and from the very outset a conceptual framework that integrates brain science and psychology. Such a framework, I assert, is more likely to yield testable hypotheses that will tie brain function and behavior together more appropriately. Today many neuropsychologists are tackling explanations of what part(s) of the brain are involved in what basic behavioral productions, and this has thus far proved fruitful. We should tackle sapience in the same way. Here I outline a few elements of this framework to show a way toward what might be possible.

First, we need to situate sapience within the general framework of the mind, both our subjective experiences and what can be objectively determined. Second, we need to see if we can tease out some functional components of sapience that might allow us to use traditional analytical methods to identify processes more finely. Of course this cannot be a goal unto itself. It can only be meaningful in the context of a more holistic concept of sapience. Third, we need to consider the evolutionary history of sapience since its capacity uniquely defines our species. Sapience and second order consciousness [i.e., consciousness of being conscious, a property unique to humans, as far as we know (Donald 1991, 9)] will be found to go hand-inhand in an evolutionary sense.

Mobus, G.E. (2011). “Net Energy and the Economy: A Primer”, The Third International Biophysical Economics Meeting, April 15-16, 2011, SUNY-ESF, Syracuse New York. PDF File

Main Thesis and Overview

From First Principles:

• All physical and mental work is explained by the laws of physics — specifically the Laws of Thermodynamics (as applied to systems far from equilibrium)
• In particular, all work depends on energy flowing from a high potential source through the work process to a low potential sink
• All economic activity depends on physical and mental work; the economy is a special case of a general energy flow system

Mobus, G.E. (2009). Peak Energy, EROI, and the Economy: Modeling Contraction in the Flow of Net Energy and Its Impact on Economic Activity, The Second International Biophysical Economics Meeting, Oct. 2009, SUNY-ESF, Syracuse New York. PDF File

First Principles — The Macro-Macro View

• The flow of energy through a system is a prerequisite for organization development in that system
• All economic activity is work in the biophysical sense
• Increasing flows of high-grade energy allow more work
• Energy from non-renewable sources, i.e. fossil fuels must necessarily take increasing amounts of work to extract, ergo increasing amounts of energy reinvested with less net energy available to the rest of the economy

Mobus, G.E. (2008). “Money and Energy”, The First International Biophysical Economics Meeting, Oct. 2008, SUNY-ESF, Syracuse New York. PDF File

An Energy Standard for Money

• Motivation
• Measurable quantity
• Meaningfulness
• Money supply pegged according to the work that can be done
• Current financial situation makes it clear that forms of money beyond cash are not real

Mobus, G.E., “;Lessons Learned from MAVRIC's Brain: An Anticipatory Artificial Agent and Proto-consciousness”, Invited Talk: 5th Intl. Conf. on Computing Anticipatory Systems, CASYS'01, Liege, Belgium, Aug. 2001. HTML version [200+k - with graphics]

Abstract: MAVRIC II is a mobile, autonomous robot whose brain is comprised almost entirely of artificial adaptrode-based neurons. The architecture of this brain is based on the Extended Braitenberg Architecture (EBA) and includes the anticipatory computing capabilities of the adaptrode synapse. We are still in the process of collecting hard data on the behavioral traits of MAVRIC in the generalized foraging search task. But even now sufficient qualitative aspects of MAVRIC’s behavior have been garnered from foraging experiments to lend strong support to the theory that MAVRIC is a highly adaptive, life-like agent. The development of the current MAVRIC brain has led to some important insights into the nature of intelligent control. Based on the nervous system of a simple invertebrate creature, this brain and its interactions with a realistic environment, never-the-less, have led to some important qualitative principles of design.

In this paper, we elucidate some of these principles and using this basis along with the work of Antonio Damasio, we develop concepts for the next generation (NG) brain. While it is a giant leap from the brain of a simple invertebrate to that of a human (as described by Damasio), the NG brain includes provisions for a proto-self reflecting the fundamental biological aspects of brain architecture as described by Damasio. We have reasons to believe that this proto-self is necessary for more advanced forms of intelligence. As with the success of the current brain, the NG brain will depend, ultimately, on the adaptive capacity (in a nonstationary world) of anticipatory processing elements.

Mobus, G.E., "Adapting Robot Behavior to Nonstationary Environments: A Deeper Biologically Inspired Model of Neural Processing" Proceedings, SPIE - International Conference: Sensor Fusion and Decentralized Control in Robotic Systems III, Biologically Inspired Robotics, Boston, MA, Nov. 6-8, 2000, pp. 98-112. HTML version [200+k - with graphics]

Abstract: Biological inspiration admits to degrees. This paper describes a new neural processing algorithm inspired by a deeper understanding of the workings of real biological synapses. It is shown that a multi-time domain adaptation approach to encoding causal correlation solves the destructive interference problem encountered by more commonly used learning algorithms. It is also shown how this allows an agent to adapt to nonstationary environments in which longer-term changes in the statistical properties occur and are inherently unpredictable, yet not completely lose useful prior knowledge. Finally, it is suggested that the use of causal correlation coupled with value-based learning may provide pragmatic solutions to some other classical problems in machine learning.

Mobus, G.E., "Foraging Search: Prototypical Intelligence", Third Internation Conference on Computing Anticipatory Systems, HEC Liege, Belgium, August 9-14, 1999. Daniel Dubois, Editor, Center for Hyperincursion and Anticipation in Ordered Systems, Institute of Mathematics, University of Liege. HTML version [128k - with graphics]

Abstract: We think because we eat. Or as Descartes might have said, on a little more reflection, "I need to eat, therefore I think."

Animals that forage for a living repeatedly face the problem of searching for a sparsely distributed resource in a vast space. Furthermore, the resource may occur sporadically and episodically under conditions of true uncertainty (non-stationary, complex and non-linear dynamics). I assert that this problem is the canonical problem solved by intelligence. It's solution is the basis for the evolution of more advanced intelligence in which the space of search includes that of concepts (objects and relations) encoded in cortical structures. In humans the conscious experience of searching through concept space we call thinking.

The foraging search model is based upon a higher-order autopoeitic system (the forager) employing anticipatory processing to enhance its success at finding food while avoiding becoming food or having accidents in a hostile world. Aforager is an anticipatory system as defined by Rosen. I present a semi-formal description of the general foraging search problem and an approach to its solution. The latter is a brain-like structure employing dynamically adaptive neurons. A physical robot, MAVRIC, embodies some principles of foraging. It learns cues that lead to improvements in finding targets in a dynamic and non-stationary environment. This capability is based on a unique learning mechanism that encodes causal relations in the neural-like processing element.

An argument is advanced that searching for resources in the physical world, as per the foraging model, is a prototype for generalized search for conceptual resources as when we think. A problem represents a conceptual disturbance in a homeostatic sense. The finding of a solution restores the homeostatic balance. The establishment of links between conceptual cues and solutions (resources) and the later use of those cues to think through to solutions of quasi-isomorphic problems is, essentially, foraging for ideas. It is a quite natural extension of the fundamental foraging model.

Mobus, G.E. and Aparicio, M., "Foraging for Information in Cyberspace", presented at CASCON'94, Toronto, Ontario, Ca., 1994, pp.179--192. PDF File

Abstract: Recent advances in autonomous robotic control may be applied to the problem of designing intelligent, mobile agents for cyberspace. This paper examines the field of behavior-based reactive systems (BBRS) as used in robotics that may be applicable in constructing intelligent agents. The problem of search in an unstructured and dynamic, distributed environment has been explored in a physical robot. Two major aspects — the use of chaos to generate stochastic path selection, and a unique associative learning mechanism that bounds the search space on subsequent runs — have been shown to be an efficient strategy for the robot in exploring a nonstationary environment. The role of chaos is to produce novel, yet constrained paths. The robot learns from experience that certain associations will increase its chances of finding mission-critical events, while others will inhibit it. On subsequent forays into the environment, the robot uses this knowledge to improve its discovery process. This paper describes the application of these mechanisms to an intelligent agent in search of information resources in a distributed, dynamic, and unstructured computer environment. We present a search for keywords, where the agent learns the node/directory/filename cues that will increase its chances of finding documents containing those keywords.

Mobus, G.E., "Toward a theory of learning and representing causal inferences in neural networks", in Levine, D.S. and Aparicio, M (Eds.), Neural Networks for Knowledge Representation and Inference, Lawrence Erlbaum Associates, 1994. HTML version [412k including graphics]

Introduction: We perceive the world to operate according to a fundamental principle of causality in spite of the seeming chaotic behavior of nature. The Universe seems to be orderly and we are able to comprehend this order at some very deep level. Some events (states of processes) cause other events, which, in turn, cause still other events. And we find, generally, that certain events tend to be associated with certain other events, which is to say, there is regularity to the Universe. This principle lies at the root of cognition and is the basis for scientific investigation. It can be viewed as the language of nature.

Mobus, G.E., "A multi-time scale learning mechanism for neuromimic processing", unpublished Ph.D. dissertation, University of North Texas,1994.

Mobus, G.E. and Paul S. Fisher, "MAVRIC's Brain", Presented at IEA/AIE-94. 1994. Available in HTML version [203k including graphics]

Abstract: MAVRIC (Mobile Autonomous Vehicle for Research in Intelligent Control) is an embodied Braitenberg vehicle that is situated in a nonstationary, dynamic environment. It is controlled fully by an artificial brain comprised solely of simulated Adaptrode-based neurons. It learns to associate various environmental cues with mission-supportive or mission-threatening factors. It can then use those cues to seek or avoid objects. MAVRIC has roughly the intelligence of a moronic snail, but it has already yielded some insights into how greater intelligence might be built on top of lesser intelligent systems, thus recapitulating the evolution of intelligence in nature.

Mobus, G.E. and Paul S. Fisher, "Foraging Search at the Edge of Chaos", Presented at Metroplex Institute of Neurodynamics Conferenceon Oscillations in Neural Networks, May, 1994. This paper appears as an invited chapter (16) in D.S. Levine, V. R. Brown and V. T. Shirey (Eds.), Oscillations in Neural Systems, Lawrence Erlbaum Associates, Publishers, Mahwah, New Jersey. Available in HTML version [221k including graphics]

Abstract: Many animal species are faced with the problem of finding sparsely distributed resources that occur (and disappear) dynamically in a huge space. Furthermore the dynamics of these resources are stochastic and probably chaotic in terms of both spatial and temporal distribution. But they are not completely random. The search strategy is called foraging. It involves two interrelated phases which dominate the behavior of the animal based on the amount of knowledge it has regarding the location and timing of the resource. In the absence of knowledge or cues foraging animals adopt a stochastic search pattern that will have a reasonably high likelihood of bringing them into encounters with the resource. With knowledge or cues, the animal switches to a more directed search behavior.

Autonomous agents such as mobile robots may need to have these capabilities in order to find mission-critical objects yet no current algorithmic or heuristic search method adequately addresses this problem. The serendipitous discovery of a quasi-chaotic oscillating neural circuit used to generate motor signals in a mobile robot has led to the development of an autonomous agent search method that resembles foraging search in a number of details.

An oscillator circuit, based on the concept of a central pattern generator (CPG) in biology, is described qualitatively. Its role in controlling the motion of a mobile robot and the effects it has on search efficiency are presented. Constraining search to potentially fruitful paths before any useful heuristics are available to the searcher is a problem of general interest in artificial intelligence. Foraging search based on chaotic oscillators may prove useful in a more general way.