For Specialists

Purpose

This page translates the Paradigm of the Great Life into operational entry points for active researchers. The goal is not to replace existing formalisms but to add an explicit “subjective leg” that yields new questions, measurements, and controls.

Common ground (all fields)

• Dual stance: Pair objective constraints (equations, conservation, statistics) with an explicit agent layer (models, priors, goals, control policies).
• Minimal agent criterion: closed-loop control that measurably changes future data; model improvement over time under constraints.
• Measures to watch: surprisal ($−log Q$), cross-entropy, KL divergence, predictive error/free energy, control cost, work from coherence/structure.

Quantum foundations and quantum control

Operational bridge

• Keep standard quantum dynamics. Add an explicit “closing the loop” layer when a measurement basis is chosen and outcomes drive feedback (measurement-based control).
• Read: 🧿Quantum Foundations, 🎛️Quantum Control & Feedback, ✅Operational Criteria.

Prompts

1. Quantum trajectories with adaptive bases

• Task: Implement real-time basis selection driven by an agent-model to minimize predictive error over a horizon.
• Measure: reduction in time-averaged negative log-likelihood of observed outcomes vs static bases; energy/information trade-off.
• Tools: quantum trajectories, Bayesian filters, reinforcement learning controllers.

2. Coherence-to-work conversion as “delegation events”

• Task: Map control sequences that convert off-diagonals (resource theory of coherence) into extractable work or mutual information.
• Measure: work per unit coherence consumed; robustness to noise; informational gain per control bit.

3. Phase as context, modulus as statistics

• Task: Design interferometric protocols where phase encodes task-relevant “context relations,” and agent choice of basis exposes them as observables.
• Measure: task performance vs phase control fidelity; ablation: remove phase control, quantify degradation.

Thermodynamics of open systems and non-equilibrium statistical physics

Operational bridge

• Dual second law: global entropy constraints + local agent-driven reductions of uncertainty using free-energy flows.
• Read: 🔥Open‑System Thermodynamics, 📊Information: Objective vs Subjective.

Prompts

1. Agentized Maxwell demons under resource budgets

• Task: Quantify net work extraction vs costs for sensing, memory, and actuation with explicit budgets.
• Measure: net work, mutual information, regret (missed work vs oracle), Pareto fronts cost↔benefit.

2. Structure growth cascades

• Task: Track energy and information gradients across cascades (photon → chemical → neural/computational → artefact).
• Measure: local decreases in predictive entropy; energy dissipation; transfer efficiency; modularity increases.

3. Control-limited self-assembly

• Task: Compare spontaneous vs guided self-assembly with the same energy input but different information/control injection.
• Measure: defect rates, yield, free-energy landscapes traversed, minimal control bits for target fidelity.

Autocatalysis, origin-of-life, and chemical networks

Operational bridge

• Minimal proto-agency: closed-loop maintenance of target concentrations under perturbations; counterfactual sensitivity.
• Read: 🧪Autocatalytic Sets & Proto‑Agency, ✅Operational Criteria.

Prompts

1. Distinguish interaction vs control in chemical sets

• Task: Apply perturbations (feeds, inhibitors) and test whether the network restores target ratios by modulating reaction pathways.
• Measure: control gain; recovery time; invariants maintained; information-to-work conversion.

2. Evolve “policies” in silico and in vitro

• Task: Use evolutionary algorithms to discover control policies (feeds/flushes) that stabilize autocatalytic sets.
• Measure: stability region size; robustness to noise; policy complexity vs performance.

3. Information budgets in metabolism

• Task: Relate sensing/memory complexity in synthetic metabolic circuits to achievable homeostasis regimes.
• Measure: phase diagrams over (information, energy) budgets; critical thresholds.

Neuroscience, cognitive science, and AI/ML

Operational bridge

• Explicitly separate Text (sensory data), Context (model/prior), Interpretation (posterior/policy). Treat the agency as closed-loop control, minimizing predictive error/free energy.
• Read: 📝Text–Context–Interpretation, 🧭Agency & Delegation.

Prompts

1. Measuring agent surprisal vs source entropy

• Task: In embodied agents, log $−log Q_t(o_t)$ over time; compare to estimated source surprisal $−log P(o_t)$.
• Measure: $KL(P \|\| Q_t)$ decay; action-triggered surprisal drops; energy cost of updates.

2. Info-cloning vs learning

• Task: Compare populations trained from scratch vs with shared priors (“cloned context”) on adaptation speed and diversity.
• Measure: convergence time; policy diversity; robustness; transfer.

3. Counterfactual control tests

• Task: Intervene on policy; verify detectably different future Texts (data) and improved objectives.
• Measure: causal effect sizes; uplift in predictive accuracy; control cost.

Complex systems and collective behavior

Operational bridge

• Quasi-organisms: stable collectives that share and reproduce Context. Balance individuality (learning) vs uniformity (info-cloning).
• Read: 🧬Hierarchies & Quasi‑Organisms.

Prompts

1. Individuality–uniformity index

• Task: Define index over ensembles (e.g., swarms, microbial consortia) that correlates with adaptability.
• Measure: performance under regime shifts vs index; trade-offs with stability.

2. Cross-level misinformation costs

• Task: Quantify failures when lower levels infer higher-level Texts incorrectly (mis-specified coarse-graining).
• Measure: error cascades; recovery policies; minimal message sets for reliable cross-level control.

3. Collective closed loops

• Task: Detect when collectives enact closed loops (sensing→update→act) at their own scale distinct from individuals.
• Measure: Granger/transfer entropy across scales; control energy localized at collective modes.

Methodological checklist (to keep the “dual leg” honest)

• Declare the agent layer: who/what owns $Q$ (the Context), what are the goals, what sensors/actuators exist?
• Specify costs: energy, time, bandwidth, memory; include them in performance metrics.
• Separate $P$ vs $Q$: estimate source distributions $P$ where possible; track $KL(P \|\| Q)$ over time.
• Design counterfactuals: interventions that would have produced different Texts if the model were wrong.
• Report misses: where the dual view adds nothing or conflicts with data; refine the agent model first.

Data and tooling suggestions

• Quantum: QuTiP, Strawberry Fields, Xanadu/IBM toolchains for feedback control.
• Thermo/non-eq: pymbar, PySINDy, noneq MD packages; information-theoretic estimators (JIDT, dit).
• Chemical networks: COPASI, BioNetGen, SBML; microfluidics for perturbation policies.
• AI/ML: PyTorch/JAX; libraries for active inference/RL; causal inference toolkits.

How to cite and contribute

• Cite: Alexander Neshmonin, Changing the Paradigm of Life: New Answers to the Old Questions (EN edition).
• Site text: CC BY 4.0. Pull requests with minimal, falsifiable prompts are welcome (link to repo if/when available).
• Contact: see 📬Contact & Updates; pointers to datasets, code, or experiment designs appreciated.

Reading map for specialists

• Start with: ✅Operational Criteria (definitions, measurements).
• Then pick your field: 🧿Quantum Foundations, 🔥Open‑System Thermodynamics, 🧪Autocatalytic Sets & Proto‑Agency, 🎛️Quantum Control & Feedback, 🧬Hierarchies & Quasi‑Organisms.
• Finally,🧑‍🎓Research Directions for compact project ideas you can run with current tools.