From: jimruttshow8596
This article explores two distinct but related concepts in the study of complexity: Effective Complexity and Integrated Information.
Effective Complexity
Effective complexity is a measure of complexity proposed by Murray Gell-Mann and Seth Lloyd [00:21:16]. It combines physical and computational notions of complexity [00:21:18].
Core Idea
Effective complexity distinguishes between the “random stuff” and the “non-random stuff” required to describe a system [00:22:59]. It utilizes concepts like entropy (a physical notion of information) and algorithmic complexity (a computational notion) [00:21:29]. The focus is on the algorithmic part needed to describe the system, excluding purely random fluctuations [00:22:03].
Examples
- Gas in a room: The effective complexity describes macroscopic properties like the percentage of gases, temperature, pressure, and air flow [00:22:09]. This is a significantly smaller amount of information (e.g., a few tens or hundreds of thousands of bits) compared to the vast amount of information (e.g., 10^30 bits) needed to describe the random motions of individual molecules [00:22:40].
- Bacterium Metabolism: For an E. coli bacterium, effective complexity would involve describing the organization of its metabolism, chemical reactions, energy utilization, and much of its DNA [00:24:04]. It excludes the exact molecular configurations or the wiggling of individual atoms [00:25:17]. While still a large number (billions of bits), it’s far less than describing every atomic motion [00:25:27].
- Engineered Systems: In industrial design challenges, such as designing a car, effective complexity is well-defined by the blueprint and manufacturing descriptions needed to achieve its functional requirements [00:47:35]. It accounts for necessary elements like alloy manufacturing without needing to specify every atom’s position [00:47:53].
Subjectivity and Coarse-Graining
The definition of effective complexity often involves a subjective element: defining what is “important” information for a given system or purpose [00:26:01]. For a bacterium, this means considering its purpose, such as taking in food and reproducing within its environment [00:26:32].
This process is related to the concept of coarse-graining, where one examines a system at a particular scale and discards information below that scale [00:29:17]. To use effective complexity, it is important to define the level of coarse-graining [00:29:47].
Integrated Information Theory (IIT)
Integrated Information Theory is a concept related to mutual information, focusing on how much information is shared and can be inferred between different parts of a system [00:51:31].
Core Idea
Proposed by Giulio Tononi, integrated information is an “intricate form of mutual information” [00:51:31]. It quantifies the degree to which the operation of different parts of a system can be inferred from each other dynamically [00:52:03]. Complex systems like brains or bacteria tend to have high integrated information [00:51:51].
Relation to Consciousness
Tononi asserts that anything with a high degree of integrated information is conscious [00:53:56]. Seth Lloyd disputes this claim [00:54:00].
“tonon just states that anything that has a lot of integrated information is conscious and I simply do not believe that error correcting codes are conscious that’s just not uh that’s in fact there one of the least conscious things I can think of” [00:53:56]
This stance aligns with panpsychism, the idea that everything is conscious [00:54:18]. While everything carries and processes information, this does not equate to consciousness [00:54:30]. Seth Lloyd suggests that the problem lies in the definition of consciousness itself [00:54:46]. As philosopher John Searle suggests, consciousness may be a process, much like digestion, involving multiple interacting biological systems (e.g., perception areas, memory, ontologies) [00:55:36].
Examples and Limitations
- Brains and Bacteria: These systems exhibit high integrated information due to their complex, coordinated processes [00:51:51].
- Error-Correcting Codes: An error-correcting code possesses high integrated information because even if many bits are corrupted, the original message can be reconstructed due to redundancy [00:52:41]. Every part of the system contains information about the message [00:52:54]. However, such codes can be quite simple in their design [00:53:09], challenging the idea that high integrated information inherently implies complexity or consciousness.
- Simple Ordered Systems: A billion bits that are all zeros or all ones have a lot of mutual information (a billion bits, as they are all the same) [00:49:50], but are not considered complex [00:50:02]. Similarly, some systems with high integrated information may not be what is intuitively considered complex [00:52:29].
Integrated information is a necessary condition for complexity (you’d expect a complex system to have it), but it is not a sufficient condition [00:50:44].
Conclusion
Both effective complexity and integrated information are attempts to quantify aspects of complexity, particularly in systems with many interacting parts. Effective complexity aims to measure the non-random, functionally significant information required to describe a system, often requiring a defined purpose or level of coarse-graining [00:26:01]. Integrated information, a more intricate form of mutual information, assesses the interdependencies and inferability between system parts, but its proposed link to consciousness remains controversial [00:53:56].
The discussion highlights that there isn’t a single, universal measure of complexity; rather, different measures are appropriate for different contexts and applications [03:32:05], depending on the specific “purpose” or “domain” of inquiry [00:27:37].