AIBearisharXiv – CS AI · Apr 157/10
🧠Researchers tested whether large language models exhibit the Identifiable Victim Effect (IVE)—a well-documented cognitive bias where people prioritize helping a specific individual over a larger group facing equal hardship. Across 51,955 API trials spanning 16 frontier models, instruction-tuned LLMs showed amplified IVE compared to humans, while reasoning-specialized models inverted the effect, raising critical concerns about AI deployment in humanitarian decision-making.
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AIBearisharXiv – CS AI · Apr 137/10
🧠Researchers challenge the applicability of Prospect Theory to Large Language Models, finding that PT parameters are unstable when models encounter epistemic uncertainty markers like "likely" or "probably." The study warns against deploying PT-based frameworks in real-world applications where linguistic ambiguity is common, raising critical questions about LLM decision-making reliability.
AINeutralarXiv – CS AI · Mar 167/10
🧠Researchers developed a supervised fine-tuning approach to align large language model agents with specific economic preferences, addressing systematic deviations from rational behavior in strategic environments. The study demonstrates how LLM agents can be trained to follow either self-interested or morally-guided strategies, producing distinct outcomes in economic games and pricing scenarios.
GeneralBearishFortune Crypto · 3d ago6/10
📰Economic research reveals that childhood exposure to male labor force struggles creates long-term workforce participation decline, as children internalize negative employment outcomes they observe in their communities. This mechanism transforms temporary labor demand shocks into persistent supply-side problems, suggesting that economic hardship's effects extend across generations through behavioral adaptation.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers demonstrate that UI-based sustainability interventions can increase energy awareness and encourage responsible LLM chatbot usage without sacrificing usability. A study combining baseline surveys with a five-day field trial found that simple design features like energy-mode switches and real-time feedback drove 55.8% adoption of efficient settings, despite baseline willingness to trade performance for sustainability being low at 39%.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce a new framework for strategic classification that accounts for behavioral biases rather than assuming perfect rationality from agents. The Prospect-Guided Strategic Framework (Pro-SF) incorporates psychological principles from prospect theory to better model real-world decision-making in adversarial machine learning contexts.
$MKR
GeneralBearishFortune Crypto · Jun 76/10
📰Only 16% of U.S. adults report feeling financially fulfilled despite earning adequate incomes, revealing a widespread psychological disconnect between salary levels and subjective financial wellbeing. Experts attribute this anxiety gap to systemic cost-of-living pressures, lifestyle inflation, and psychological factors beyond actual bank balances.
AINeutralarXiv – CS AI · Jun 45/10
🧠Researchers developed Neetyabhas, an agent-based simulation framework that models pandemic policy decisions under real-world uncertainty, incorporating individual behavioral choices and imperfect data. Using reinforcement learning, the model demonstrates that masks and vaccines effectively reduce outbreak severity when policies account for implementation errors and measurement gaps.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers demonstrate that optimal control in Markov decision processes with catastrophic failure states naturally produces prospect-theory-like behaviors—including S-shaped value functions and loss aversion—without requiring utility curvature or probability weighting. The mechanism emerges purely from the mathematical structure of Bellman optimality when agents face absorbing failure states, with results validated across 495 configurations and multiple learning paradigms.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers have developed a framework using LLM-based economic agents to simulate macroeconomic expectations in survey experiments, demonstrating that these AI agents can generate expectation distributions comparable to human survey data. The framework successfully captures human-like reasoning patterns when equipped with personal characteristics, prior beliefs, and external information, offering potential applications for economic modeling and expectation formation research.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers develop a game-theoretic framework modeling how students collectively adopt responsible or opportunistic AI use in academic assessments. The study reveals that small, well-designed changes to assessment incentives can trigger rapid behavioral shifts toward ethical AI practices, whereas policy statements alone typically fail to change behavior.
AINeutralarXiv – CS AI · May 276/10
🧠Research comparing 120 base and aligned language model pairs reveals that alignment training makes models more normative but less descriptive of actual human behavior. Base models predict real human choices in multi-round strategic games 10 times better, while aligned models excel only in single-shot, textbook scenarios where human behavior follows rational expectations.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers found that base large language models do not replicate human motivated reasoning patterns when tested across four political studies. Unlike humans who adjust their reasoning based on desired conclusions, LLMs show different behavioral patterns, raising concerns about using these models for opinion simulation and argument assessment tasks.
AINeutralarXiv – CS AI · Apr 76/10
🧠A randomized control trial reveals that incentive structures significantly influence how humans use generative AI in creative tasks. When participants were rewarded for originality rather than just quality, they produced more diverse collective output by using AI more selectively for brainstorming and editing rather than copying suggestions verbatim.
