# Game Theory Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Game Theory", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

IOSG: TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman

The article presents a critical analysis of Bittensor (TAO), a decentralized AI marketplace, comparing its economic model to Elon Musk's early investment in OpenAI, where funders have no guaranteed return. TAO acts as a funding layer, distributing tokens to subnet operators (akin to labs or researchers) based on their subnet token performance. However, subnets are under no obligation to return any value—such as AI models, data, or services—back to the TAO ecosystem. This creates a risk of value extraction, where developers may use TAO incentives for research and then commercialize成果 independently. The bear case highlights the lack of binding mechanisms, potential capital drain through token inflation, and competition from well-funded centralized AI giants. The optimistic view argues that continuous resource needs may naturally retain subnets within Bittensor, leveraging crypto’s ability to aggregate resources at scale, similar to Bitcoin’s success with computation. Ultimately, investing in TAO is a speculative bet on a coordination miracle—where incentives align to keep valuable AI projects within the ecosystem—rather than a traditional equity-like claim on output. The outcome distribution is highly skewed: most scenarios lead to niche use or value leakage, while a few could result in TAO becoming a foundational decentralized AI asset.

marsbit04/14 10:45

IOSG: TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman

marsbit04/14 10:45

TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman

The article, titled "TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman," presents a critical analysis of the Bittensor (TAO) project. It argues that Bittensor functions as a decentralized AI marketplace where TAO tokens fund AI research via subnets. However, the author highlights a fundamental flaw: subnet operators have no obligation to return any value, such as AI models or profits, back to the TAO ecosystem or its token holders. This structure is likened to Elon Musk's early investment in the non-profit OpenAI, which later commercialized its technology without returning value to its initial benefactor. The bear case posits that Bittensor is essentially a wealth transfer from crypto speculators to AI researchers ("miners"). Subnets can use TAO incentives for development and then take their successful products elsewhere, leaving TAO holders with diluted tokens from inflation and no captured value. The lack of enforced equity or binding mechanisms means the project relies on a "hope" that subnet tokens maintain value. The optimistic perspective counters that two factors could create a successful, self-sustaining economy: 1) AI's perpetual and massive resource needs could incentivize subnets to stay for continued funding, and 2) crypto has a proven ability to aggregate resources through token incentives, as seen with Bitcoin and Ethereum. The conclusion states that investing in TAO is a bet on a博弈论 (game theory) miracle—that soft incentives alone will be enough to keep the best subnets within the ecosystem and create a flywheel effect. This outcome is possible but represents a highly skewed, low-probability success scenario amidst significant risks of failure.

marsbit04/13 14:01

TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman

marsbit04/13 14:01

Only 43% Return on $1, Why Are 87% of Polymarket Players Losing Money?

In prediction markets like Polymarket, data from 72 million trades reveals that 87% of traders lose money, with an average return of just $0.43 per dollar risked on low-probability contracts. This underperformance stems from behavioral biases, such as the "longshot bias," where traders systematically overvalue cheap contracts (e.g., 1¢ priced outcomes actually win only 0.43% of the time). Winning traders (the top 13%) use five key game theory principles: 1. **Expected Value (EV):** Calculating long-term average returns based on subjective vs. market-implied probabilities. 2. **Mispricing:** Identifying deviations between contract prices and actual outcome frequencies, avoiding overpriced low-probability bets. 3. **Kelly Criterion:** Optimizing bet sizing to maximize growth while managing risk, often using fractional Kelly to reduce volatility. 4. **Bayesian Updating:** Adjusting probability estimates rationally as new information emerges, rather than overreacting or ignoring data. 5. **Nash Equilibrium:** Recognizing market structure shifts (e.g., maker vs. taker dynamics) and adapting strategies—especially in emotional markets (e.g., sports/entertainment) where mispricing is common. As professional market makers enter, inefficiencies shrink, emphasizing the need for disciplined, math-driven trading over emotional speculation.

marsbit03/30 08:11

Only 43% Return on $1, Why Are 87% of Polymarket Players Losing Money?

marsbit03/30 08:11

1 Dollar Return Rate Only 43%, Why Are 87% of Polymarket Users Losing Money?

In the prediction market Polymarket, analysis of 72.1 million trades reveals that 87% of wallets lose money, while only 13% consistently profit. The key difference lies in the application of game theory and mathematical strategies, not luck. Five core formulas separate winners from losers: 1. **Expected Value (EV)**: Winners calculate EV to identify undervalued contracts, while most traders rely on intuition. Makers (limit order placers) profit by waiting for positive EV opportunities, while takers (market buyers) lose ~1.12% per trade on average. 2. **Mispricing**: Low-probability contracts (e.g., priced at 1¢) are systematically overpriced, with actual win rates as low as 0.43% (a -57% deviation). High-probability contracts are often undervalued. Takers overpay for "cheap" lottery-like bets, while makers capture this inefficiency. 3. **Kelly Criterion**: Used for optimal position sizing. It maximizes long-term growth but is often applied fractionally (e.g., 1/2 or 1/4 Kelly) to reduce volatility. 4. **Bayesian Updating**: Profitable traders adjust probabilities rationally as new information emerges, unlike emotional overreactions or inertia from others. 5. **Nash Equilibrium**: The market structure evolves with participant behavior. In emotional markets (e.g., sports, entertainment), mispricing creates opportunities for contrarian strategies. As professional market makers enter, spreads tighten, and inefficiencies shrink. The conclusion: Persistent losses stem from emotional trading, overpaying for low-probability bets, and neglecting mathematical discipline. The winning minority uses these formulas to exploit market biases systematically.

Odaily星球日报03/30 08:03

1 Dollar Return Rate Only 43%, Why Are 87% of Polymarket Users Losing Money?

Odaily星球日报03/30 08:03

Bitcoin Game Theory Framework Tracks Market Coordination — Here’s How

The Bitcoin Game Theory framework analyzes market structure by tracking coordination between miners, investors, traders, and institutions. It aims to detect early signs of alignment breakdown, which has historically preceded major downturns. For example, it signaled exits in May 2022 and October 2025, avoiding subsequent declines of 54% and 45.5%, respectively. Currently, the market is transitioning as long-term whales finished accumulating in late 2025, while new whales are still building positions. This dual accumulation may delay the rally but could lead to a stronger bull run if both groups converge. Despite Bitcoin trading near 2021 prices, analysts project growth to $200,000 by 2027-2030 and potentially $500,000 by 2033-2035, emphasizing that current stability reflects building underlying pressure rather than bearish sentiment.

bitcoinist03/29 07:21

Bitcoin Game Theory Framework Tracks Market Coordination — Here’s How

bitcoinist03/29 07:21

Ending Zero-Sum Games: An In-Depth Research Report on Web3 Incentive Engineering and Odyssey Behavioral Dynamics

The report "Ending Zero-Sum Games: A Deep Dive into Web3 Incentive Engineering and Odyssey Behavioral Dynamics" analyzes the evolution of Web3 incentive mechanisms, arguing that traditional airdrop and points-based models have led to inefficiency, Sybil attacks, and low user retention. It proposes a shift from volume-based metrics to value-based unit economics, where user lifetime value (LTV) must exceed customer acquisition cost (CAC). The new paradigm defines incentives as a combination of Credit (e.g., SBTs), Privileges (e.g., governance rights), and Revenue Rights (e.g., real yield). A key framework classifies users into three behavioral archetypes: Gamma (profit-driven farmers), Beta (engaged explorers), and Alpha (long-term builders). Successful incentive design must encourage migration from Gamma to Alpha by making authentic contribution more profitable than farming. The report introduces technical solutions to ensure incentive compatibility (IC): - A Dynamic Difficulty Adjustment (DDA) mechanism to auto-calibrate task complexity. - A Proof of Value (PoV) model to measure "contribution density" (liquidity, time, governance activity). - A ZK-based behavioral attestation layer for private, Sybil-resistant user verification. Finally, the Odyssey model is envisioned to evolve from a marketing campaign into a native, embedded protocol (GaaS - Growth-as-a-Service) with interoperable credit across ecosystems, fostering a shift from speculative engagement to sustainable, value-aligned collaboration.

marsbit02/11 13:47

Ending Zero-Sum Games: An In-Depth Research Report on Web3 Incentive Engineering and Odyssey Behavioral Dynamics

marsbit02/11 13:47

From 100% Rebate to 20%: The Game Theory Behind Polymarket's Fee Adjustment

Polymarket recently adjusted its fee structure for 15-minute cryptocurrency prediction markets, moving from a 100% fee rebate to a 20% rebate for market makers. This change aims to combat "latency arbitrage" bots that exploited tiny time delays between exchange price updates and Polymarket’s price adjustments, harming market makers and reducing liquidity. Initially, full rebates were introduced to incentivize market makers to stay despite bot activity. After data showed a significant drop in fee volume—indicating reduced bot activity—the platform reduced rebates to 20% for a trial period. This reflects an ongoing effort to balance fairness among market makers, automated traders, and regular users. The article also references ongoing "money printer" strategies used by some successful traders and emphasizes Polymarket’s role in providing a competitive, strategy-driven environment.

marsbit01/14 13:33

From 100% Rebate to 20%: The Game Theory Behind Polymarket's Fee Adjustment

marsbit01/14 13:33

Ethereum's Year of Interoperability: A Deep Dive into EIL, a Grand Experiment in Entrusting 'Trust' to Game Theory?

Ethereum is entering a major phase of mass adoption in 2026, driven by the development of the Ethereum Interoperability Layer (EIL). EIL is not a new blockchain but a set of standards and protocols designed to connect Ethereum’s Layer 2 (L2) networks seamlessly. It aims to standardize state proofs and message passing between L2s, enabling native interoperability without altering their core security models. Currently, L2s operate as isolated environments with separate signatures, assets, and user experiences. EIL, combined with Account Abstraction (ERC-4337) and a trust-minimized messaging layer (XLP), allows users to perform cross-chain transactions with a single signature, abstracting away complexities like gas fees and repeated authorizations. Unlike traditional bridges or intent-based solutions, EIL avoids introducing new trust assumptions like solvers. Instead, it relies on XLP providers to facilitate instant transactions, with Ethereum L1 serving as a fallback enforcement layer via slashing mechanisms in case of malfeasance. However, EIL faces challenges in balancing efficiency and security. Its model shifts trust from technical verification to economic incentives and penalties, raising questions about sustainability under market volatility, scalability in multi-chain environments, and the economic feasibility of liquidity provision. Despite these open questions, EIL represents a significant experiment in expanding Ethereum’s interoperability while preserving its core values of decentralization and self-custody.

marsbit01/12 09:59

Ethereum's Year of Interoperability: A Deep Dive into EIL, a Grand Experiment in Entrusting 'Trust' to Game Theory?

marsbit01/12 09:59

Underlying Algorithms and Social Robustness: A Christmas Reflection on the Evolution of Principles and Their Game Theory Logic

In this Christmas reflection, Ray Dalio explores the role of principles as foundational algorithms for individual and societal decision-making. He argues that principles shape our utility functions and define what we value most, even in extreme scenarios. Dalio examines the compatibility of modern behavioral norms with religious teachings, emphasizing that while supernatural elements may lack empirical support, the core ethical principles across religions—such as reciprocity, empathy, and social cooperation—are remarkably consistent and serve as public goods that reduce transaction costs and enhance collective welfare. He defines "good" as behavior that maximizes social utility (positive externalities) and "evil" as actions that harm the system (negative externalities). Good character, in this view, is an asset that promotes collective well-being. However, Dalio warns that society is currently on a "downward trajectory," where consensus on shared principles has eroded, replaced by self-interest maximization. This decline manifests in cultural decay, rising inequality, and a lack of moral exemplars. Despite technological progress, he stresses that technology alone cannot resolve conflicts; it merely amplifies existing values. The solution lies in rebuilding a shared rulebook centered on mutual benefit and systemic optimization, leveraging our advanced capabilities to address global challenges.

marsbit12/29 12:21

Underlying Algorithms and Social Robustness: A Christmas Reflection on the Evolution of Principles and Their Game Theory Logic

marsbit12/29 12:21

Underlying Algorithms and Social Robustness: A Christmas Reflection on the Evolution of Principles and Their Game Theory Logic

Bridgewater founder Ray Dalio reflects on the importance of principles as core intangible assets that serve as underlying algorithms for decision-making. He argues that principles shape individual utility functions and define what people are willing to live and die for. Dalio examines the compatibility of modern behavioral norms with religious teachings, emphasizing that while supernatural elements may lack empirical support, the core ethical principles across religions—such as reciprocity, empathy, and social cooperation—are remarkably consistent and serve as public goods that reduce societal transaction costs. He defines “good” as behavior that maximizes total social utility (positive externalities) and “evil” as actions that harm collective well-being. Good character, in economic terms, is an asset that commits to maximizing group welfare. However, Dalio warns that society is on a “downward trajectory,” where consensus on shared principles has eroded. Self-interest maximization has replaced moral frameworks, leading to a loss of social capital and increased systemic risks like inequality, violence, and institutional distrust. He concludes that although technology offers powerful tools for progress, it is a double-edged sword. The key to solving systemic crises lies in rebuilding a shared rulebook grounded in reciprocal altruism and collective optimization—not in technical advancement alone.

深潮12/29 12:14

Underlying Algorithms and Social Robustness: A Christmas Reflection on the Evolution of Principles and Their Game Theory Logic

深潮12/29 12:14

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