Problem: The inclusion of the observer into scientific observation entails a vicious circle of having to observe the observer as dependent on observation. Second-order science has to clarify how its underlying circularity can be scientifically conceived. Method: Essayistic and conceptual analysis, sporadically illustrated with agent-based experiments. Results: Second-order science - implying science in general - is fundamentally and ineluctably circular. Implications - The circularity of second-order science asks for analytical methods able to cope with phenomena of complex causation and “synchronous (...) asynchrony,” such as tools for analyzing non-linearly interacting dynamics, decentralized, clustered networks and in general, systems of complex interacting components. Key Words: Observer dependency, second-order science, complexity, information, intentionality. (shrink)

Problem: Can communication emerge from the interaction of “self-referentially closed systems,” conceived as operating solely on the base of the “internal” output of their onboard means? Or in terms of philosophical conceptions: can communication emerge without (“outward” directed) “intention” or “will to be understood”? Method: Multi-agent simulation based on a conceptual analysis of the theory of social systems as suggested by Niklas Luhmann. Results: Agents that co-evolutionarily aggregate probabilities on how to cope with their environment can structurally couple and generate (...) a form of “eigenbehavior” that retrospectively (i.e., by an observer) might be interpreted as communication. Implications: The “intention” or the “will to be understood,” as prominently claimed to be indispensable in communication by theoreticians such as Jürgen Habermas, can be seen as a retrospective ascription to an emergent property of complex interaction. Constructivist content: The paper attempts to base constructivist reasoning on data generated in simulations. (shrink)

Upshot: Does constructivism need to assert its validity or is it more appropriate to assume its possibility, discuss its consequences and try to deliver arguments that show it is a viable epistemology?

Upshot: Circularity is multifarious indeed. Some aspects, however, seem related to current developments and therefore may deserve more attention than others.

Upshot: The differentiation between society being emergent or sui generis seems to correspond to the question of whether the development of interaction, in particular communication, should better be considered bottom-up, top-down or as a sort of circular concurrency of bottom-up and top-down causes. This is reminiscent of the philosophical debate about the implications of the terms emergence and downward causation.

Upshot: I present reflections on the particularities of second-order science in response to the commentaries on my paper, as well as comments on the limitations of verbal analytical attempts to grasp the implicit circularity of observer-inclusion.

In this commentary I take up thoughts about the sociology of time and Luhmann’s theory to consider whether automated future option maximization can be extended with a social dimension.

Problem: Is constructivism contradicted by the reductionist determinism inherent in digital computation? Method: Review of examples from dynamical systems sciences, agent-based modeling and artificial intelligence. Results: Recent scientific insights seem to give reason to consider constructivism in line with what computation is adding to our knowledge of interacting dynamics and the functioning of our brains. Implications: Constructivism is not necessarily contradictory to digital computation, in particular to computer-based modeling and simulation. Constructivist content: When viewed through the lens of computation, in (...) many of its aspects constructivism seems in line with what currently is held to be valid in science. (shrink)

Context: Referring to a recent proposition by Kauffman about the “fundamental nature of circularity in cybernetics and in scientific work in general,” I try to advance this insight with the help of system scientific concepts and a computational model. Problem: Often circularity seems to be taken as a metaphor that does not provide a firm epistemological base that fosters analysis. Method: The methodology builds on mathematics, computer-based modeling, and reasoning. Results: By building on conceptual suggestions for grasping the micro-macro difference (...) of complex systems in terms of computational power, circularity can be conceived of as an emerging macro-level phenomenon. Implications: I show that the seemingly irritating - and traditionally evaded - concept of circularity is a fundamental and ubiquitous phenomenon in complex systems that can be grasped on a firm physical basis open to computational analysis. The proposal could support constructivist reasoning and help to eventually bridge the disconcerting gap between the humanities and natural sciences. Constructivist content: Circularity is a fundamental principle in the conception of second-order cybernetics and in particular in the observation of observing systems, as suggested by von Foerster. Trying to set it up on a firm analytical basis could advance the constructivist approach and further support it in becoming the contemporary scientific epistemology it deserves to be. (shrink)

In order to capitalize on the self-referentiality of interpretation, as marked by Gasparyan, I suggest considering semiosis - the process of signification - as a systemic consequence of interacting….

Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Upshot: While I agree with Gordana Dodig-Crnkovic’s IC approach, I am uncertain about two points: first about whether constructivism needs yet another etiquette in order to be considered a viable conception, and second whether the focus on information and computation carries the risk of directing attention away from other crucial aspects of the approach.

Open peer commentary on the article “Exploration of the Functional Properties of Interaction: Computer Models and Pointers for Theory” by Etienne B. Roesch, Matthew Spencer, Slawomir J. Nasuto, Thomas Tanay & J. Mark Bishop. Upshot: Why computer models of constructivist processes can enhance constructivist matters even though the models will always “seem incomplete.”.

Context: By proposing to regard objects as “tokens for eigenbehavior,” von Foerster’s seminal paper opposes the intuitive subject-object dualism of traditional philosophy, which considers objects to be instances of an external world Problem: We argue that this proposal has two implications, one for epistemology and one for the demarcation between the natural sciences and the humanities. Method: Our arguments are based on insights gained in computational models and from reviewing the contributions to this special issue. Results: Epistemologically, von Foerster’s proposal (...) suggests that what is called “reality” could be seen as an ensemble of eigenforms generated by the eigenbehavior that arises in the interaction of multiple dynamics. Regarding science, the contributions to this special issue demonstrate that the concept of eigenbehavior can be applied to a variety of disciplines from the formal and natural sciences to the humanities. Its universal applicability provides a strong argument for transdisciplinarity, and its emphasis on the observer points in the direction of an observer-inclusive science. Implications: Thinking in eigenbehavior may not only have implications for tearing down the barriers between sciences and humanities, a better understanding of eigenbehaviors may also have profound effects on our understanding of ourselves. This also opens the way to innovative behavior design/modification technologies. (shrink)

Open peer commentary on the article “What Can the Global Observer Know?” by Diana Gasparyan. Upshot: I consider the possibility of replacing the global observer with a collective observer and ask whether the insights generated by such a collective observer would have to be considered subject to non-linear interactions.

: In the commentaries, a number of intriguing projections of Future State Maximization on a multitude of different systems are suggested, which raised the need to clarify certain ….

Context: Many AI and machine-learning techniques are primarily focused on past-to-future extrapolations of statistical regularities in large amounts of data. We introduce a method that builds on an….