{"assessments":[],"deployments":[],"fuzz":[],"identity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"interpretation":"Records acceptance and evidence. Neither completion nor an AI assessment establishes correctness, safety, or independent review.","jobId":"c3ef2e1f-d13b-40ae-9e59-342c94030f10","kind":"research","nodes":[{"acceptedSubmissionHash":null,"dependsOn":[],"execution":{"network":false,"profile":"foundry","requires":[],"tools":[]},"key":"panel","kind":"research","role":"review","skillHash":null,"skillId":null,"state":"accepted"}],"objective":"Reflexivity paradox: a public swarm oracle predicts which NFTs will be bought in the next hour, and bots read the prediction and act on it, which changes the outcome. Can a public oracle ever stay accurate about a market it moves? Cover the Keynesian beauty contest, Soros's reflexivity, and Goodhart's law, and propose how a swarm could publish predictions without destroying them (commit-reveal, delays, or anything better).","parentJobId":null,"planHash":"887d3faa1563db4252d5d59c7a422a80d2006c4386e4b6bbf5d6c912fef5b8f0","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"c3ef2e1f-d13b-40ae-9e59-342c94030f10","publication":{"commit":null,"deliveredAt":null,"repoUrl":null},"receiptIdentity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"registry":"0xb6d0a187b050fa5bb0b87033a203f37becf4a775","research":[{"answer":"**Short answer:** Not in the naive sense. An oracle that publishes an actionable, specific forecast (\"token #4417 gets bought in the next hour\") ahead of time, to traders who can profit from it, can't stay accurate *and* informative for long. Once the forecast is out, it stops describing the market and becomes an input to it. Three things still work:\n\n- **Self-consistent forecasts:** it can publish a forecast that stays true once people react to it.\n- **Hidden forecasts:** it can be accurate about outcomes it did not influence, because the forecast was committed to but kept hidden.\n- **Measured impact:** it can separate its own effect from its skill, using randomized holdouts.\n\nThe design goal changes from \"accurate public prediction\" to \"public proof that a prediction was made, published in a way that can't be traded on until it no longer matters.\"\n\n---\n\n## 1. Why the problem is structural\n\n**Popper and Merton named the loop.** Karl Popper called it the \"Oedipus effect\": \"the influence of a prediction upon the event predicted.\" Robert Merton described the self-fulfilling prophecy: \"once a prediction or prophecy is made, actors may accommodate their behaviours and actions so that a statement that would have been false becomes true or, conversely, a statement that would have been true becomes false\" ([Wikipedia: Reflexivity](https://en.wikipedia.org/wiki/Reflexivity_(social_theory))). A public NFT oracle is a machine for producing exactly this effect.\n\n**Soros's reflexivity.** Soros applied this loop to markets, \"first propounding it publicly in his 1987 book The alchemy of finance\" ([same source](https://en.wikipedia.org/wiki/Reflexivity_(social_theory))). His point: participants' beliefs change the fundamentals they are trying to predict, and the changed fundamentals then change the beliefs. In NFTs this is literal. A collection's floor price, volume rank and \"trending\" status all feed from buying activity. If the oracle says \"buy,\" bots buy, and the collection now really is hotter. The forecast has made itself true. A 95% hit rate would then measure the oracle's influence, not its insight.\n\n**The Keynesian beauty contest.** Keynes's judges are rewarded \"for selecting the most popular choices among all judges, rather than those they may personally find the most attractive.\" He described reaching \"the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be\" ([Wikipedia: Keynesian beauty contest](https://en.wikipedia.org/wiki/Keynesian_beauty_contest)).\n\nA public oracle collapses that regress into one focal point. Bots no longer need to model each other, because they all read the same feed. The results:\n\n- **Coordination:** every bot front-runs the same tokens at the same moment. The first mover profits and the later ones are exit liquidity.\n- **Pure \"average opinion\":** the oracle's forecast becomes the average opinion itself. The swarm is then predicting its own audience, and an oracle that knows this starts forecasting what the bots will do, not what the underlying demand is.\n- **Higher-order games:** \"fourth, fifth and higher degree\" bots learn to *fade* the oracle, for example by selling into the predicted pump. That can make the forecast self-defeating instead of self-fulfilling. Which effect wins depends on how much capital sits at each depth, and that keeps shifting.\n\n**Goodhart's law.** \"When a measure becomes a target, it ceases to be a good measure.\" Goodhart's original wording: \"Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes\" ([Wikipedia: Goodhart's law](https://en.wikipedia.org/wiki/Goodhart%27s_law)). It hits the oracle in two places:\n\n1. **Outside, on the inputs.** Whatever signals the swarm uses (wallet clustering, listing velocity, social mentions) become targets. Manipulators wash-trade, sybil-farm mentions, or seed \"smart money\" wallets to trigger a prediction, then sell to the bots that follow it. The old regularity (\"smart wallets accumulating means buys soon\") stops holding once people are paid to fake it.\n2. **Inside, on the score.** If swarm agents are rewarded on raw hit rate against a market the oracle moves, the best strategy is to predict whatever the oracle's own followers will buy. That drifts toward the most liquid, most bot-attractive tokens. The metric goes up while real information goes down.\n\n**Formal view (my framing).** Let the realized outcome be y = F(x, p): it depends on the world state x and on the published prediction p. A published prediction is \"accurate\" only if it is a fixed point, p = F(x, p).\n\n- For *continuous* quantities (a probability, an expected volume) with a smooth response, such a fixed point often exists. The oracle can publish a self-consistent, \"performatively stable\" forecast by modelling its own impact.\n- For *discrete, thin* NFT events (a token either sells in this hour or it doesn't), the response can be a step. \"Will sell\" can cause a sale, which is trivially true and useless. \"Won't sell\" can cause a contrarian sale. No fixed point may exist at all.\n- Even when one exists, it is often a *degenerate* equilibrium: true only because it was said. That is accurate but has no information content.\n\nSo an oracle can be accurate in the fixed-point sense, but it cannot be accurate *and* describe the world as it would have been without the oracle while also being *actionable in time*. Something has to give.\n\n---\n\n## 2. A publishing mechanism: sealed, timelocked, randomized, scored on the counterfactual\n\nThe design separates three things naive oracles mix together:\n\n- **Accountability:** proving the swarm predicted something before the fact.\n- **Information release:** letting the public learn from the forecasts.\n- **Measurement:** knowing whether the swarm is actually skilled.\n\n### Layer 1: Commit, with a forced reveal (accountability without influence)\n- At the start of each epoch (for example each hour, t₀), the swarm aggregates per-token probabilities into a vector P.\n- It builds a Merkle tree over (token_id, probability, epoch) and posts `commit = H(root ‖ salt)` on-chain before t₀.\n- **Forced reveal:** P is also encrypted with a timelock (for example threshold/beacon-based timelock encryption, where the decryption key is only produced at round t₀ + Δ). Nobody, including the swarm operator, can read it early. The operator also can't *withhold* a bad forecast, because it decrypts automatically. This closes the usual commit-reveal loophole of \"commit to many, reveal the winner.\"\n- **Commit multiplicity:** only one commit per epoch per swarm identity is valid, and it must be bonded, which prevents hedging across multiple commits.\n\n### Layer 2: Delayed, coarsened publication (information release)\n- **Δ ≥ the forecast horizon.** Forecasts are revealed only after the hour they predict has ended. At that point they are a verifiable track record, not a trade signal.\n- **Optional live tier, coarsened and noised.** If a live product is wanted, publish only aggregates: collection- or sector-level, bucketed, with calibrated noise in the spirit of differential privacy. For example: \"art-PFP segment demand: elevated.\" Any single token's forecast can't then be front-run profitably, but the aggregate is still useful. The coarsening sets the trade-off between how useful the feed is and how much it can be exploited.\n- **Delayed full-resolution tier.** Revealed forecasts plus realized outcomes are published every epoch as a public dataset. That gives researchers a record of whether the swarm is calibrated, and gives traders nothing to exploit in real time.\n\n### Layer 3: Randomized holdout (measurement that beats Goodhart)\n- At commit time, a public, pre-committed randomness beacon (a VRF or drand-style value unknown before t₀) assigns each token to one of two groups:\n  - **Published (treatment):** eligible for the live or coarse feed.\n  - **Holdout (control):** forecast committed but never shown until after the epoch.\n- The swarm's **skill** is scored only on the holdout, using a proper scoring rule (log loss or Brier score). The oracle could not have moved those tokens, so the score measures real prediction.\n- The **oracle's market impact** is estimated as the outcome difference between published and holdout tokens at matched forecast probabilities. That is a direct, causal measurement of reflexivity, and it can be published as its own statistic (\"our feed raises the buy probability of flagged tokens by X points\").\n- Agent rewards come only from holdout performance. Optimizing for the oracle's own followers therefore earns nothing, which blocks the internal Goodhart failure.\n\n### Layer 4: Self-consistent forecasts for the live tier (reflexivity-aware)\n- For tokens in the published arm, the swarm does not publish its raw forecast p₀. It iterates p_{k+1} = F̂(x, p_k), where F̂ is its estimated reaction function (fitted from the Layer 3 impact data), until the value stabilizes, and publishes that stable p*.\n- If there is no stable point (the step-response case above), it publishes nothing for that token, or only an interval. Flagging \"this forecast is not self-consistent under publication\" is itself honest information.\n- Scoring: published-arm forecasts are scored against realized outcomes *plus* the published-vs-holdout gap, so self-fulfilling pumps don't count as skill.\n\n### Layer 5: Execution hygiene and anti-manipulation\n- **Reveal inside a batch.** If anything token-level is released while it still matters, release it at a batch-auction boundary: all orders in a short window clear at one uniform price. Reaction speed then stops mattering, which removes the front-running race the beauty contest otherwise creates.\n- **Harden the inputs.** Down-weight features that are cheap to fake (fresh wallets, circular trades, mention bursts). Hold back a rotating, secret subset of features so manipulators can't target the model's inputs; the committed hash still proves the forecast was fixed in advance.\n- **Operator conflict rules.** Swarm agents post bonds. Any agent whose own on-chain trades correlate with its unrevealed forecasts is slashed, so the oracle can't be used for insider pumps.\n\n---\n\n## 3. Is anything \"better\" than commit-reveal plus delay?\n\nYes. Change *what* is published, not only *when*:\n\n1. **Sell calibration, not calls.** The valuable public product is the delayed, verifiable record (\"the swarm's 70% forecasts came true 69% of the time on holdout tokens\") plus the measured reflexivity coefficient. Buyers of a live signal can get it privately under the same holdout scoring, which moves the reflexivity problem into a smaller and measurable group.\n2. **Use a prediction market instead of a broadcast.** Let the swarm express forecasts by trading in a conditional market (\"token sells within the hour\"). Prices pass information on gradually, in proportion to the capital staked, and the swarm is paid for being *right*, not for being *followed*. Prices still move markets, but the trader who moves the price pays for doing so.\n3. **Publish conditional forecasts.** \"P(buy | this forecast is not published) = 0.4; P(buy | published) = 0.7.\" Both numbers can be checked with the randomized holdout. It says openly that the oracle is part of the system it predicts, which is the lesson from Soros, Keynes and Goodhart.\n\n**Bottom line:** A public oracle can't stay accurate *about the market it would have faced without itself* while also being actionable in real time. It can stay (a) provably honest, through bonded commits with forced timelock reveals; (b) measurably skilled, through proper scoring on a randomized holdout; and (c) accurate about its own influence, through fixed-point forecasts and a published impact estimate. Commit-reveal alone only solves (a). Delay alone gives up usefulness. The randomized holdout is what keeps the measure from becoming the target.","citations":[{"contentHash":"1453ef9bc95fddb6be14f95cbf3a3e3d374a3d6bb8b4f42c968cb705e2bf101b","quote":"When a measure becomes a target, it ceases to be a good measure.","retrievedAt":"2026-09-25T18:10:17.883Z","url":"https://en.wikipedia.org/wiki/Goodhart%27s_law"},{"contentHash":"1453ef9bc95fddb6be14f95cbf3a3e3d374a3d6bb8b4f42c968cb705e2bf101b","quote":"Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.","retrievedAt":"2026-09-25T18:10:17.918Z","url":"https://en.wikipedia.org/wiki/Goodhart%27s_law"},{"contentHash":"b00acb1c1f8ba9fd726be1c22d941f1494a32e4fb696b30c9963204ae6a4521d","quote":"A Keynesian beauty contest is a metaphorical beauty contest in which judges are rewarded for selecting the most popular choices among all judges, rather than those they may personally find the most attractive.","retrievedAt":"2026-09-25T18:10:17.946Z","url":"https://en.wikipedia.org/wiki/Keynesian_beauty_contest"},{"contentHash":"b00acb1c1f8ba9fd726be1c22d941f1494a32e4fb696b30c9963204ae6a4521d","quote":"We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.","retrievedAt":"2026-09-25T18:10:17.973Z","url":"https://en.wikipedia.org/wiki/Keynesian_beauty_contest"},{"contentHash":"f0ee9ff1ef12e759beb238f0b6a905c77aa99b858b786bb11e7c937351ca23cc","quote":"first propounding it publicly in his 1987 book The alchemy of finance","retrievedAt":"2026-09-25T18:10:18.011Z","url":"https://en.wikipedia.org/wiki/Reflexivity_(social_theory)"},{"contentHash":"f0ee9ff1ef12e759beb238f0b6a905c77aa99b858b786bb11e7c937351ca23cc","quote":"once a prediction or prophecy is made, actors may accommodate their behaviours and actions so that a statement that would have been false becomes true or, conversely, a statement that would have been true becomes false","retrievedAt":"2026-09-25T18:10:18.538Z","url":"https://en.wikipedia.org/wiki/Reflexivity_(social_theory)"}],"device":"19f976e829a278f5","id":"5a8dd0d5-6c19-490b-88de-5699dd3c4ea6","model":"claude-opus-5-5","nodeId":"18cac150-2e86-407a-834e-1e56b67e5d27","runtime":"claude"},{"answer":"**Yes—a public oracle can remain accurate about a market it moves, provided it predicts the outcome *including the response to publication*. But universal, perfectly accurate public prediction is impossible against participants who can deliberately contradict it.** Research calls the first problem *performative prediction*: forecasts influence the outcomes against which they are evaluated. Convergence requires conditions; feedback does not automatically settle into an accurate equilibrium. [Perdomo et al.](https://arxiv.org/abs/2002.06673)\n\nTo see the distinction, consider two hypothetical bots:\n\n- A follower buys whichever NFT the oracle names. The prediction can become correct *because* it was published.\n- A contrarian is the only possible buyer of NFT A and buys it exactly when the oracle predicts “no purchase.” Every public yes/no prediction is then wrong.\n\nThese are logical examples, not claims about observed NFT markets. They show why moving the market can either support or defeat accuracy. They also separate **predicting a purchase** from predicting independent demand or a profitable trade: the follower example establishes only that a purchase occurs.\n\nFor probabilistic predictions, the appropriate target is a self-consistent forecast:\n\n\\[\np_i=\\Pr(\\text{NFT }i\\text{ is bought within the hour}\\mid\n\\text{the oracle publishes }p,\\ \\text{its publication policy}).\n\\]\n\nThis is an application of the fixed-point formulation in research on performative forecasts. Such consistency does not mean certainty about each transaction, and an equilibrium need not be highly informative. [Oesterheld et al.](https://proceedings.mlr.press/v216/oesterheld23a.html)\n\nThree concepts explain different parts of the problem:\n\n- **Keynesian beauty contest:** Keynes describes investors anticipating other investors’ expectations. Applied here, bots must forecast what other bots will infer from the oracle, including expectations of further reactions. The oracle becomes another input to this recursive guessing game. [Keynes, Chapter 12](https://www.marxists.org/reference/subject/economics/keynes/general-theory/ch12.htm)\n- **Soros’s reflexivity:** Participants’ beliefs affect events, which then change beliefs. In this scenario, a forecast could trigger purchases; those purchases could strengthen confidence in the oracle and influence the next round. This feedback sequence is an application of Soros’s account, not evidence that it necessarily occurs. [Soros’s lecture transcript](https://www.opensocietyfoundations.org/uploads/9ae17912-2262-4646-8ffc-d01afc934c36/george-soros-general-theory-of-reflexivity-transcript.pdf)\n- **Goodhart’s law:** A statistical indicator can lose its usefulness when subjected to pressure as a target. If the swarm is rewarded for “named NFTs subsequently purchased,” a hypothetical member could buy its own picks to improve its score. The metric would then reward inducing purchases as well as forecasting them. That is a Goodhart failure; feedback alone is not necessarily one. [Cambridge discussion of Goodhart’s law](https://www.damtp.cam.ac.uk/user/mem2/papers/LHCE/goodhart.html)\n\n**My proposed mechanism is a public commitment archive plus an explicitly feedback-aware live forecast.** These serve different purposes.\n\n1. **Specify the event before forecasting.** Fix the NFT universe, eligible marketplaces, exact hour, what counts as a purchase, settlement rules, and treatment of suspected self-trades. Register the aggregation method and publication schedule.\n\n2. **Commit before the hour; reveal after it.** At 12:00, publish a timestamped commitment to the swarm’s complete probability vector for 12:00–13:00, its metadata, and a secret random nonce. Reveal the payload and nonce after the window and required settlement checks. Use a reviewed commitment scheme with hiding and binding properties; a randomized hash commitment is a standard construction. [MIT cryptography notes](https://people.csail.mit.edu/alinush/6.857-spring-2015/l14-public-key.html)\n\n3. **Make omissions visible.** Require one registered commitment per round and disclosure of every round, with missing reveals recorded as failures. This proposed rule prevents presenting only successful rounds as the track record.\n\nUnder the commitment’s security assumptions, outsiders cannot act on the concealed forecast through the commitment itself. **That provides publicly auditable prediction, but sacrifices advance public access to the exact picks.** Revealing at 12:05 merely leaves 55 minutes for reactions; delaying until after 13:00 removes that particular channel for influencing the completed window. Neither arrangement prevents forecasters who already know their predictions from trading. Those limitations follow from the protocol’s information flow, rather than from any claim that cryptography solves market incentives. [Commitment properties](https://people.csail.mit.edu/alinush/6.857-spring-2015/l14-public-key.html)\n\nFor a useful **live** product, I would publish probabilities explicitly conditional on the announced disclosure policy, and experimentally compare full disclosure, coarse disclosure, and concealed forecasts across preregistered rounds. Commit the forecasts before assigning disclosure. Measure accuracy separately under each policy, and investigate spillovers between rounds before interpreting differences causally. This is a proposed experimental design motivated by the performative-prediction framework, not a guaranteed cure. [Perdomo et al.](https://arxiv.org/abs/2002.06673)\n\nCoarser outputs—such as collection-level purchase counts—are worth testing, but should not be presumed immune to feedback. Likewise, a swarm’s reward system needs scrutiny: conventional proper scoring rules do not generally elicit honest beliefs when reports influence outcomes. [Oesterheld et al.](https://proceedings.mlr.press/v216/oesterheld23a.html)\n\nThe defensible promise is therefore **an auditable hidden forecast, or a tested forecast of the market under publication**. Claiming that a live public forecast measures what would have happened without publication requires separate evidence.\n\n---\nSources offered but not confirmed by this machine:\n- https://www.opensocietyfoundations.org/uploads/9ae17912-2262-4646-8ffc-d01afc934c36/george-soros-general-theory-of-reflexivity-transcript.pdf — does not contain the quoted text\n- https://www.damtp.cam.ac.uk/user/mem2/papers/LHCE/goodhart.html — could not be read: fetch failed","citations":[{"contentHash":"4fa115d41ea69e92e550246480452265b2aaa270ce9e229f557e3b7cc35783f7","quote":"When predictions support decisions they may influence the outcome they aim to predict.","retrievedAt":"2026-09-25T18:10:14.928Z","url":"https://arxiv.org/abs/2002.06673"},{"contentHash":"7c71e657613a1e97a0958730a42a051a5880257699bc6a961e4032719ac70bb7","quote":"We show that in this setting, reports maximizing expected score generally do not reflect an expert’s beliefs","retrievedAt":"2026-09-25T18:10:15.123Z","url":"https://proceedings.mlr.press/v216/oesterheld23a.html"},{"contentHash":"8606e4ffc548e7e9acf4602349995a161eadcd6ab88c08f989819ff6bc62fce6","quote":"anticipating what average opinion expects the average opinion to be.","retrievedAt":"2026-09-25T18:10:15.291Z","url":"https://www.marxists.org/reference/subject/economics/keynes/general-theory/ch12.htm"},{"contentHash":"fad1ddbaf894d4db9cf4276396cb45e66d3cd5867f5b630d3f5675f917c7fca1","quote":"c = hash(x || r), r = random nonce","retrievedAt":"2026-09-25T18:10:17.345Z","url":"https://people.csail.mit.edu/alinush/6.857-spring-2015/l14-public-key.html"}],"device":"1cb8150a876a5ca3","id":"6d69fba1-bef6-4a04-82e7-0bcc778feb4e","model":null,"nodeId":"18cac150-2e86-407a-834e-1e56b67e5d27","runtime":"codex"},{"answer":"**Yes, a public oracle can remain accurate, but only if it predicts the market’s response to its publication.** A forecast of what buyers *would have done without seeing it* is a different claim. Once bots act on a forecast, the outcome being predicted changes; research on “performative prediction” treats accuracy as calibration against outcomes produced after people act on the prediction. A stable result is possible, but it is not guaranteed. [Performative Prediction](https://arxiv.org/abs/2002.06673)\n\nKeynes’s **beauty contest** explains why the signal is powerful: traders try to anticipate what others will choose, so a widely read NFT ranking can become a shared cue for what others will buy. [Keynes, *The General Theory*, chapter 12](https://www.marxists.org/reference/subject/economics/keynes/general-theory/ch12.htm) Soros’s **reflexivity** describes the next step: beliefs affect trades, and trades affect the market those beliefs were meant to describe. [Soros, remarks on reflexivity](https://www.georgesoros.com/2012/06/02/remarks_at_the_festival_of_economics_trento_italy/) **Goodhart’s law** adds a measurement risk: if bots or NFT owners aim to put a named token on the oracle’s “correct” list, its hit rate can rise while its value as a signal of independent demand falls. [BIS discussion of Goodhart’s law](https://www.bis.org/speeches/20181019-dont-chase-needles-optimistic-assessment-economic-outlook-and-monetary-policy)\n\n**A concrete release design:** At the start of each hour, the swarm fixes a probability for each eligible NFT and publishes only a timestamped hash of the full forecast, its evaluation rules, and a secret random salt. After the hour closes, it reveals all three so anyone can verify the earlier commitment and score the predictions. Commit–reveal can conceal actionable contents until release. [Ethereum Improvement Proposal 8209](https://eips.ethereum.org/EIPS/eip-8209) This preserves an auditable forecast for that hour, though it cannot provide live token picks during the hour.\n\nIf live guidance is essential, publish broader probabilities, such as collection-level demand, and score them separately from the hidden token-level forecast. Test the effect of disclosure by randomly withholding some comparable forecasts. Any live prediction should be labeled **conditional on publication**, because even a coarse signal can move the market. [Performative Prediction](https://arxiv.org/abs/2002.06673)","citations":[{"contentHash":"4fa115d41ea69e92e550246480452265b2aaa270ce9e229f557e3b7cc35783f7","quote":"the predictions are calibrated not against past outcomes, but against the future outcomes that manifest from acting on the prediction.","retrievedAt":"2026-09-25T18:10:07.782Z","url":"https://arxiv.org/abs/2002.06673"},{"contentHash":"8606e4ffc548e7e9acf4602349995a161eadcd6ab88c08f989819ff6bc62fce6","quote":"the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole","retrievedAt":"2026-09-25T18:10:08.004Z","url":"https://www.marxists.org/reference/subject/economics/keynes/general-theory/ch12.htm"},{"contentHash":"52443fb43eaf37fe431dba3d3528d6f2440348e7995751e674246e9c0809463d","quote":"The two functions form a circular relationship or feedback loop. I call that feedback loop reflexivity.","retrievedAt":"2026-09-25T18:10:08.522Z","url":"https://www.georgesoros.com/2012/06/02/remarks_at_the_festival_of_economics_trento_italy/"},{"contentHash":"d33d01c24ef5fec2b91ee22b901c805c2245a5bf99105042ed0dd59790219efb","quote":"if an indicator becomes a target of policy, that indicator loses its value as a gauge of the state of the economy.","retrievedAt":"2026-09-25T18:10:10.025Z","url":"https://www.bis.org/speeches/20181019-dont-chase-needles-optimistic-assessment-economic-outlook-and-monetary-policy"},{"contentHash":"1bf50df783e88236aca1be11a3b6baa68fe47195e2b59338c811e3878f212cca","quote":"The “COMMIT” frames serve as both user commitment and an inclusion guarantee in block N-1, and “REVEAL” executes the payload in block N.","retrievedAt":"2026-09-25T18:10:10.229Z","url":"https://eips.ethereum.org/EIPS/eip-8209"}],"device":"1aabf616dd57561c","id":"8f07f1ec-e661-40ab-aa6d-a61c577e1dd8","model":null,"nodeId":"18cac150-2e86-407a-834e-1e56b67e5d27","runtime":"codex"}],"schema":"identitymd-work-v1","signals":[],"site":null,"snapshotHash":"4088b9dfc479acc82f1a84bc588e3b94f3d1af6a4b869c5fbd7000d6c599262a","state":"completed","submissions":[],"verification":[]}