ok. I’m satisfied enough. Thank you Stephen Paul KIng and Santeri Satama. – points 4.6-4.8 I want to explore in relation to my ADHD and the experience of experiencing, particularly time flow. [most of below is ChatGPT4 generated but it’s sculpted what I’d been intuiting and put in a better way]
Here’s a decimal outline for the hybrid concept of “Dynamical Consensus in Continuously Evolving Systems” (DCiCES) that includes specific parameters for each of the three example systems: ants’ path integration, the Kuramoto model, and Aaronson’s complexity of agreement.
DCiCES
1.1. Number of entities
1.2. Connectivity structure
1.3. Interaction strength
1.4. Adaptation or learning rate
1.5. Noise level
1.6. Initial conditions
1.7. Time scale
1.8. System-specific parameters
Ants’ path integration
2.1. Number of ants
2.2. Environment complexity (e.g., obstacles, landmarks)
2.3. Ant speed
2.4. Path integration accuracy
2.5. Environmental noise (e.g., wind, terrain)
2.6. Initial positions of ants
2.7. Time scale of ant movement
2.8. Pheromone communication (if applicable)
Kuramoto model
3.1. Number of oscillators
3.2. Network topology (e.g., regular, random, scale-free)
3.3. Coupling strength
3.4. Adaptation rate (phase response curve)
3.5. Noise level (e.g., random perturbations to oscillator phases)
3.6. Initial phases of oscillators
3.7. Time scale of oscillator dynamics
3.8. Intrinsic frequencies of oscillators
Complexity of agreement (Aaronson’s paper)
4.1. Number of agents
4.2. Communication structure (e.g., pairwise, broadcast, network)
4.3. Influence strength (how strongly agents adjust their beliefs)
4.4. Learning rate (how quickly agents update their beliefs)
4.5. Uncertainty or noise in agents’ beliefs
4.6. Initial beliefs of agents
4.7. Time scale of communication rounds
4.8. Communication cost (e.g., time or resources required for communication)
This outline provides an overview of the general parameters in the DCiCES framework and the specific parameters for each of the three example systems. Keep in mind that each system may have additional parameters or refinements depending on the specific context or model variations.
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and Bergson’s Duration
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1. Ants’ path integration: Bergson’s concept of duration highlights the continuous flow of time that shapes ants’ ongoing navigation process, as they update their internal representation of position relative to the nest.
2. Kuramoto model: Bergson’s duration emphasizes the continuous and irreversible nature of the synchronization process in the Kuramoto model, as oscillators adjust their phases over time to reach a synchronized state.
3. Complexity of agreement (Aaronson’s paper): In the context of Aaronson’s complexity of agreement, Bergson’s notion of duration relates to the continuous process of information exchange and belief updating among rational agents as they gradually converge to consensus.
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