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Bamberg Mathematical Consciousness Science Initiative (BAMΞ)

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BAMΞ's Research

BAMΞ facilitates research in Mathematical Consciousness Science (MCS), an emerging field in Consciousness Science that develops and applies mathematics to understand the nature, mechanisms, functions, and contents of conscious experience.

BAMΞ supports research across the entire spectrum of Mathematical Consciousness Science (MCS). This website highlights some of the core themes of BAMΞ’s research. For more information about upcoming research projects, see our research activities page.

Research on Theories of Consciousness

Theories of consciousness, also called models of consciousness, are hypotheses about how conscious experiences and the subject matter of the natural sciences, most notably the brain, relate. Research at BAMΞ aims to:

  1. Improve and clarify theories of consciousness, where mathematical or proto-mathematical concepts are concerned;
  2. Explore problems and limits of contemporary theories of consciousness, and how these might be overcome;
  3. Construct new theories and models of consciousness, based, for example, on novel empirical findings or novel metaphysical conceptions;
  4. Contribute to the exploration and critical self-reflection of the theory-building process in Consciousness Science.

Research at BAMΞ is not tied to a single theory, but open to and supportive of theories across research programmes.

Research on Experimental Paradigms in Consciousness Science

The Scientific Study of Consciousness is an inherently empirical discipline. Its progress rests on empirical observations in carefully designed experiments. While BAMΞ does not carry out empirical research itself, it contributes to the development of mathematical tools, methods, and models to support and improve experimental research, including, for example:

  1. Modelling of experimental paradigms in Consciousness Science, for example for the falsification of theories of consciousness;
  2. Analysis, formalisation, and improvement of empirical tests and measures of consciousness, for example in light of the structure of conscious experiences;
  3. Development of statistical tools for empirical research, for example where Neural and Computational Correlates of Consciousness are concerned;
  4. Construction of a formal basis for measurement procedures, most notably in the form of a measurement theory for Consciousness Science.

Research on Methodological Questions in Consciousness Science

A third area of research at BAMΞ is concerned with the methods of Consciousness Science. Next to research on empirical and theoretical methods (cf. above), BAMΞ's methodological research comprises, for example, work on:

  1. Mathematical representations of conscious experience, for example in the context of Mathematical Phenomenology or structural approaches;
  2. Structural methodologies in Consciousness Science, in particular those in a potential structural turn of the field;
  3. Foundations of new ways of model-building in the field, for example regarding Computational Phenomenology;
  4. Exploration of the applicability of methodological tools from other disciplines within Consciousness Science, e.g. No-Go Theorems from physics.

BAMΞ’s research on these methodological questions makes use of both scientific and philosophical perspectives. It intersects with and contributes to the mind sciences as well as philosophy, where philosophy of science and mathematical philosophy are concerned.

Research on AI Consciousness

Due to the vast developments of Artificial Intelligence (AI) in the last years, questions pertaining to a mind of artificial systems have become particularly pressing. Research at BAMΞ is concerned with the question of whether AI systems can have conscious experiences—the question of whether AI systems can “feel”—and with the form and content of those experiences, if they are indeed possible. This includes questions like:

  1. Can AI systems be conscious? Or more precisely, which AI systems can be conscious, if any? This is the question of whether AI systems can have conscious experiences at all, independently of what the experiences are.
  2. Empirical tests for consciousness in AI systems. Research at BAMΞ investigates operational procedures that can be applied to AI systems to infer whether they have—or are capable of having—conscious experiences. These operational procedures are called C-tests.
  3. Theoretical tests for consciousness in AI systems, including both scientific and philosophical methods. A particularly important example thereof is research that uses theories of consciousness to assess whether AI systems are or can be conscious. Here, BAMΞ's research places a particular emphasis on the reliability of such assessments. Are the theoretical constructs across theories of consciousness rigorous and detailed enough to warrant inferences regarding the question of AI consciousness in societal contexts?
  4. Which conscious experiences can AI systems have, if any? This is the question of the phenomenal character of conscious experiences of AI systems: what is it like to be an AI system? Are the conscious experiences of AI systems anything like human conscious experiences? If so, what are the similarities and differences? If not, is there anything that can be said about AI's experiences? Research at BAMΞ explores methods to answer these questions.
  5. Which types of computation are necessary or sufficient to support AI consciousness? This question comprises the question of which forms of computation can support consciousness—can a Turing computation, say?—and the question of which specific properties a computation would have to exhibit to support artificial consciousness, if any does.
  6. Does consciousness matter for existential risk? This is the question of whether conscious AI systems in general, and self-conscious AI systems in particular, pose a particular problem for alignment or existential threats.

BAMΞ’s research on these questions makes use of mathematical tools, mathematical methods, and mathematical models to develop answers to these questions. Because AI systems are mathematically defined, rigorous answers to all of these questions may be in need of mathematical approaches.

 

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