Add scraped data for 2504.21579

Author: miklosme

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+{
+  "arxivId": "2504.21579",
+  "title": "Uncertainty, bias and the institution bootstrapping problem",
+  "abstract": "Abstract. Institutions play a critical role in enabling communities to manage common-pool resources and avert tragedies of the commons. Prior research suggests institutions emerge when universal participation yields greater collective benefits than non-cooperation. However, a fundamental issue arises: individuals typically perceive participation as advantageous only after an institution is established, creating a paradox—how can institutions form if no one will join before a critical mass exists? We term this conundrum the institution bootstrapping problem and propose that misperception—specifically, agents’ erroneous belief that an institution already exists—could resolve this paradox. By integrating well-documented psychological phenomena—including cognitive biases, probability distortion, and perceptual noise—into a game-theoretic framework, we demonstrate how these factors collectively mitigate the bootstrapping problem. Notably, unbiased perceptual noise (e.g., noise arising from agents’ differing heterogeneous physical or social contexts) drastically reduces the critical mass of cooperators required for institutional emergence. This effect intensifies with greater diversity of perceptions, suggesting that variability among agents’ perceptions facilitates collective action. We explain this counter-intuitive result through asymmetric boundary conditions: proportional underestimation of low-probability sanctions produces distinct outcomes compared to equivalent overestimation. Furthermore, the type of perceptual distortion—proportional versus absolute—yields qualitatively different evolutionary pathways. These findings challenge conventional assumptions about rationality in institutional design, highlighting how “noisy” cognition can paradoxically enhance cooperation. Finally, we contextualize these insights within broader discussions of multiagent system design, noise and cooperation, and disobedience and collective action. Our analysis underscores the importance of incorporating human-like cognitive constraints—not just idealized rationality—into models of institutional emergence and resilience.",
+  "summary": "This paper explores the \"institution bootstrapping problem\"—the challenge of establishing a cooperative institution (like a shared resource management system) when individual participation is only advantageous after a critical mass of participants exists.  It proposes that cognitive biases, like misperceiving the existence of an institution or the cost of non-participation, can actually *facilitate* the formation of institutions by lowering the required threshold of initial cooperators.\n\nKey points for LLM-based multi-agent systems:\n\n* **Bounded rationality:**  Perfect rationality assumptions in traditional game theory may not hold for real-world agents, including LLMs.  Incorporating biases and uncertainty, inherent in LLMs, can lead to more realistic and potentially more cooperative outcomes.\n* **Noise benefits:**  Even unbiased \"noise\" (e.g., individual variations in perception) in a multi-agent LLM system can paradoxically improve cooperation by creating asymmetries that favor institution formation.  Diversity in LLM agents, therefore, could be beneficial.\n* **Bootstrap problem:** LLMs, like human agents, can get stuck in non-cooperative equilibria.  This research suggests ways to overcome this, such as introducing biases or diversity in LLM agents.\n* **Social Simulation:**  The proposed game-theoretic model combined with the Moran process provides a framework to simulate and analyze LLM-based multi-agent interactions, taking into account both individual and population level dynamics.\n* **Emergence:**  The paper highlights the importance of considering bounded rationality and stochasticity in designing LLM-based multi-agent systems. These seemingly negative factors can potentially be leveraged to enhance the emergence of beneficial collective behaviors.",
+  "takeaways": "This research paper explores how uncertainty and bias can paradoxically aid in the bootstrapping of institutions (cooperative structures) within multi-agent systems.  Here's how a JavaScript developer working with LLM-based multi-agent applications can translate these insights into practical web development scenarios:\n\n**1. Simulating Bounded Rationality with Noise:**\n\n* **Scenario:** Building a multi-agent marketplace where LLMs negotiate prices and quantities of goods.  Instead of assuming perfect rationality (LLMs having complete information and maximizing utility), introduce noise into their decision-making.\n* **Implementation:**\n    * **Perceptual Noise:** Add random Gaussian noise to the LLM's evaluation of offers. For example, if an LLM receives an offer valued at `10`, add a small random value: `offerValue + gaussianNoise(0, 1)`. This simulates imperfect understanding of offer value.  You can use a library like `ml-matrix` or implement your own Gaussian noise function.\n    * **Action Noise:** Introduce randomness in the actions chosen by the LLMs. For instance, with a small probability, make an LLM accept a suboptimal offer or propose a slightly different counter-offer.\n* **Framework/Library:** LangChain, Transformers.js.\n\n**2. Exploring Bias in LLM Interactions:**\n\n* **Scenario:** Developing a collaborative writing application where multiple LLMs contribute to a document.  Introduce bias towards certain writing styles or topics.\n* **Implementation:**\n    * **Prompt Engineering:** Fine-tune or prompt LLMs to exhibit specific biases, like preferring formal language, focusing on a particular theme, or exhibiting a certain personality.\n    * **Reward Shaping:**  During reinforcement learning fine-tuning, reward the LLMs for behaviors that align with the desired bias.\n* **Framework/Library:** LangChain, Transformers.js.\n\n**3. Modeling Uncertainty in Multi-Agent Communication:**\n\n* **Scenario:** Creating a multi-agent chat application for customer support.  Model uncertainty in LLM understanding of user queries.\n* **Implementation:**\n    * **Confidence Scores:**  Utilize and expose the LLM's confidence scores for generated responses. If the confidence is low, the LLM could ask clarifying questions or escalate the issue to a human agent.\n    * **Probabilistic Responses:** Have LLMs generate multiple responses with associated probabilities, reflecting uncertainty in the best answer. The application can then choose a response based on these probabilities or present them to the user.\n* **Framework/Library:** LangChain, Transformers.js.\n\n\n**4. Asymmetric Boundary Conditions:**\n\n* **Scenario:**  Designing a decentralized autonomous organization (DAO) governed by LLMs. Implement voting mechanisms that consider potential underestimation of low-probability events.\n* **Implementation:**\n    * **Weighted Voting:**  Give more weight to votes on low-probability, high-impact events to compensate for the tendency to underestimate their likelihood.\n    * **Scenario Planning:** Integrate LLM-based scenario planning tools that explicitly consider unlikely but impactful scenarios.\n* **Framework/Library:** Web3.js, ethers.js (for blockchain integration).\n\n\n\n**5. Experimenting with Diversity of Perceptions:**\n\n* **Scenario:**  Developing an LLM-based game with multiple competing agents. Introduce diverse biases and noise levels among the agents.\n* **Implementation:**\n    * **Agent Populations:** Create distinct populations of LLMs, each with different biases and noise parameters.  Observe how the diversity of perceptions impacts the game dynamics.\n    * **Evolutionary Algorithms:** Use evolutionary algorithms to evolve the biases and noise parameters of the LLMs over time, potentially leading to more robust or adaptable agents.\n* **Framework/Library:** TensorFlow.js, Neataptic.js (for evolutionary algorithms).\n\n\n\nBy applying these principles in JavaScript, developers can create more realistic, robust, and potentially even more cooperative LLM-based multi-agent systems. These examples illustrate how bridging theoretical research with practical web development can lead to innovative and impactful applications of this cutting-edge technology.",
+  "pseudocode": "No pseudocode block found. However, there are mathematical formulas presented. Equation (1) describes the replicator equation, and equation (2) describes the Fermi function for imitation probability. These can be translated into JavaScript as follows:\n\n**Equation (1): Replicator Equation**\n\n```javascript\nfunction replicatorEquation(x_i, fitness_i, averageFitness) {\n  // x_i: Current proportion of strategy i\n  // fitness_i: Fitness of strategy i\n  // averageFitness: Average fitness of all strategies\n  const dx_i = x_i * (fitness_i - averageFitness);\n  return dx_i;\n}\n\n// Example usage:\nlet x_c = 0.2; // Proportion of cooperators\nlet fitness_c = 10; // Fitness of cooperators\nlet averageFitness = 8; // Average fitness\nlet dx_c = replicatorEquation(x_c, fitness_c, averageFitness);\nconsole.log(\"Change in proportion of cooperators:\", dx_c); \n\n```\n*Explanation:* This function calculates the change in the proportion of a given strategy within a population based on its fitness relative to the average fitness. It models how strategies propagate or decline based on their success.\n\n\n\n**Equation (2): Fermi Function (Imitation Probability)**\n\n```javascript\nfunction fermiFunction(fitness_i, fitness_j, gamma) {\n  // fitness_i: Fitness of imitating strategy\n  // fitness_j: Fitness of current strategy\n  // gamma: Inverse temperature (selection intensity)\n\n  const exponent = gamma * (fitness_i - fitness_j);\n  const probability = 1 / (1 + Math.exp(exponent));\n  return probability;\n}\n\n\n// Example Usage\nlet fitness_imitator = 12;\nlet fitness_current = 8;\nlet gamma = 0.5; // Moderate selection intensity\nlet probability = fermiFunction(fitness_imitator, fitness_current, gamma);\nconsole.log(\"Probability of imitation:\", probability);\n\n\n```\n*Explanation:* This function calculates the probability of an agent switching strategies based on the fitness difference between its current strategy and the strategy it might imitate.  The `gamma` parameter controls how strongly the probability depends on the fitness difference.  A higher `gamma` leads to a more deterministic imitation process, where agents almost always imitate higher-fitness strategies.  A lower `gamma` means that imitation is more random, and lower-fitness strategies have a reasonable chance of being imitated.\n\n\nThese JavaScript snippets provide a starting point for developers interested in experimenting with the concepts presented in the research paper.  More complex simulations would require additional code to define the environment, agents, payoffs, and update rules.  However, these core functions demonstrate how the mathematical foundations can be translated into executable code.",
+  "simpleQuestion": "How can noisy perception solve the institution bootstrapping problem?",
+  "timestamp": "2025-05-01T05:02:39.566Z"
+}