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Rational agent

A rational agent is a theoretical construct in artificial intelligence, economics, and decision theory representing an idealized decision-maker that perceives its environment and selects actions expected to maximize its performance measure or utility given available information, prior knowledge, and objectives.^[1]^[2] This model assumes access to probabilistic beliefs about outcomes and a utility function encoding preferences, leading to choices via expected utility maximization under uncertainty.^[2]^[3] In artificial intelligence, the rational agent framework, as formalized by Russell and Norvig, underpins agent architectures that operate in dynamic environments by integrating perception, reasoning, and action to achieve optimal results relative to a specified criterion of success.^[1] Key elements include the agent's percept sequence (observations over time), action repertoire, and performance measure, with rationality defined not by infallible correctness but by expected maximization given constraints like partial observability and nondeterminism.^[1] This approach has influenced practical systems in areas such as autonomous planning, game-playing algorithms, and reinforcement learning, where agents learn policies approximating rationality through trial and environmental feedback.^[1] Despite its foundational role, the perfect rationality ideal faces empirical challenges from bounded rationality, introduced by Herbert Simon, which emphasizes that real agents—human or artificial—operate under cognitive limits, incomplete information, and time pressures, often resulting in satisficing (selecting adequate rather than optimal options) rather than exhaustive optimization.^[4]^[5] Simon's work, grounded in observations of decision processes in organizations and problem-solving tasks like chess, highlights how computational complexity and search costs preclude perfect evaluation, prompting models that incorporate heuristics and approximation.^[4] This critique has spurred developments in behavioral economics and AI, shifting focus toward robust, resource-efficient agents that perform effectively despite imperfections.^[5]

Definition and Principles

Core Definition

A rational agent is an autonomous entity that perceives its environment via sensors and acts upon it through effectors, selecting actions that maximize its expected performance given the available percepts and prior knowledge.^[6] This definition emphasizes behavior over internal structure, where "rational" means doing the right thing—achieving the highest possible expected utility relative to a specified performance measure, rather than perfect foresight or unbounded computation.^[1] The performance measure defines success criteria, such as goal achievement, resource efficiency, or survival, tailored to the agent's environment, which may be fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous, and single- or multi-agent.^[6] Rationality in this framework is ideal and task-specific, acknowledging that no agent can guarantee optimality in all environments due to computational limits or incomplete information; instead, it requires actions optimal ex ante based on beliefs about outcomes and their utilities.^[1] For instance, in a stochastic environment like weather forecasting, a rational agent maximizes expected utility over probabilistic outcomes rather than deterministic success.^[6] This contrasts with notions of rationality in economics, where agents maximize utility under constraints like budget and preferences, but shares the core principle of consistent preference ordering and information-based decision-making.^[7] The agent's function maps percept histories to actions, with rationality evaluated by how well this function aligns with utility maximization across possible sequences; deviations arise from environmental uncertainty or limited prior knowledge, not inherent irrationality.^[1] Empirical tests of rationality involve measuring performance against benchmarks, such as success rates in simulated environments, though real-world applications reveal bounded rationality where computational approximations substitute for exact optimization.^[6]

Utility Maximization and Expected Utility Theory

In the framework of rational agency, utility maximization refers to the principle that an agent selects actions which optimize a utility function representing its preferences over possible outcomes, where higher utility values indicate more desirable states. This approach assumes the agent has a complete and transitive preference ordering over alternatives, enabling consistent decision-making to achieve the highest possible utility.^[1] In deterministic environments, the rational choice is the action yielding the maximum utility; however, real-world settings often involve uncertainty, necessitating extensions to handle probabilistic outcomes.^[8] Expected utility theory (EUT), developed by John von Neumann and Oskar Morgenstern in their 1944 book Theory of Games and Economic Behavior, formalizes decision-making under risk by positing that rational agents maximize the expected value of utility, computed as the sum of utilities of outcomes weighted by their probabilities.^[9] The theory derives from four axioms of rational preferences: completeness (all alternatives are comparable), transitivity (consistent rankings), continuity (preferences are continuous over lotteries), and independence (preferences between lotteries are unaffected by common outcomes).^[10] These axioms ensure the existence of a utility function u such that the expected utility of a lottery L with outcomes x_i and probabilities p_i is EU(L) = Σ p_i u(x_i), and the agent prefers lotteries with higher EU.^[11] For rational agents in artificial intelligence, EUT underpins utility-based agents, which maintain a utility function over world states and a probability distribution over possible percept sequences to compute the maximum expected utility (MEU) for actions: a rational agent chooses action a that maximizes EU(a | e) = Σ_{outcomes} P(outcome | a, e) u(outcome), where e denotes evidence or percepts.^[8] This MEU principle, as articulated in standard AI texts, allows agents to trade off among conflicting goals and handle incomplete information, outperforming simpler goal-based agents in stochastic domains.^[1] Empirical tests in economics, such as Allais's 1953 paradox experiments, have revealed violations of independence, prompting behavioral critiques, yet EUT remains a normative benchmark for rationality due to its coherence and predictive power in aggregate data.^[12]

Performance Measures and Rationality Criteria

Performance measures for rational agents quantify the degree to which an agent's actions achieve its specified objectives within a given task environment. In the PEAS framework, which describes agent task environments through Performance measure, Environment, Actuators, and Sensors, the performance measure serves as the objective criterion for success, often expressed numerically to allow comparison of action outcomes.^[6] For instance, in autonomous vehicle control, a performance measure might aggregate metrics such as distance traveled without collision, adherence to traffic rules, and fuel efficiency, weighted according to priorities.^[13] Rationality criteria evaluate whether an agent consistently selects actions that optimize these performance measures under uncertainty. A rational agent, for any percept sequence, chooses the action expected to maximize the performance measure based on available evidence and prior knowledge, rather than achieving perfect outcomes in hindsight.^[14] This criterion aligns with normative decision theory, where rationality requires maximizing expected utility, formalized as the sum of utilities of possible outcomes weighted by their subjective probabilities.^[2] Expected utility theory, axiomatized by von Neumann and Morgenstern in 1944, underpins this by assuming agents' preferences satisfy completeness, transitivity, continuity, and independence, ensuring consistent risk attitudes and choice under probabilistic outcomes.^[2] The rationality criterion is environment-dependent and information-constrained: an agent is rational relative to its percepts and computational resources, not omniscient foresight.^[13] Deviations from ideal maximization, such as due to incomplete information or bounded computation, do not inherently violate rationality if the agent exhausts feasible deliberation to approximate the optimum.^[15] In practice, performance measures must be precisely defined to avoid ambiguity; vague or misaligned measures can lead to suboptimal or unintended agent behaviors, as seen in reinforcement learning where reward functions proxy utilities but require careful engineering to reflect true goals.^[6]

Historical Development

Philosophical Origins

The concept of a rational agent traces its philosophical roots to ancient Greece, particularly Aristotle's Nicomachean Ethics (circa 350 BCE), where human agency involves practical reason (phronêsis) guiding deliberation and choice toward ends such as eudaimonia (human flourishing). Aristotle described rational action through the practical syllogism, in which agents identify goals (major premise) and available means (minor premise) to produce intentional behavior, distinguishing deliberate choice from mere impulse or habit.^[16] This framework posits the rational soul's deliberative part as directing action to achieve the good, with virtuous agents excelling by selecting context-appropriate means after reflective inquiry into possibilities.^[16] In the Enlightenment, David Hume advanced an instrumental view of rationality in A Treatise of Human Nature (1739–1740), arguing that reason serves passions by discovering causal connections between desires and actions but cannot independently motivate or evaluate ends. Hume famously termed reason the "slave of the passions," emphasizing that agents act rationally by efficiently pursuing preference-based goals through probabilistic means-end reasoning, without reason dictating intrinsic values.^[17] Immanuel Kant, in contrast, elevated rational agency to a deontological ideal in works like the Groundwork of the Metaphysics of Morals (1785), defining it as autonomous self-legislation under practical reason, where agents act from duty via maxims universalizable as laws, transcending inclinations or empirical desires.^[18] Kant's finite rational beings thus embody freedom through reason's capacity to impose normative constraints, prioritizing moral consistency over consequential outcomes. Jeremy Bentham's utilitarianism in An Introduction to the Principles of Morals and Legislation (1789) introduced a proto-formal method for rational decision-making via the felicific calculus, a quantitative assessment weighing intensities, durations, certainties, and extents of pleasures and pains to maximize net utility.^[19] This consequentialist approach framed agents as calculators of aggregate happiness, influencing later economic and decision-theoretic models by operationalizing rationality as optimization under uncertainty.^[19] These strands—Aristotelian teleology, Humean instrumentalism, Kantian autonomy, and Benthamite computation—collectively underpin the rational agent as a goal-oriented entity employing reason to navigate actions amid beliefs, desires, and constraints, laying groundwork for formalized theories in economics and artificial intelligence.^[20]

Economic Formalization

The formalization of the rational agent in economics emerged during the marginalist revolution of the 1870s, marking a shift from classical labor theories of value to models emphasizing subjective preferences and utility maximization under constraints. William Stanley Jevons, in his 1871 work The Theory of Political Economy, portrayed economic agents as individuals who allocate resources to equate marginal utilities per unit of expenditure, thereby achieving optimal satisfaction given limited means.^[21] This approach, paralleled independently by Carl Menger and Léon Walras, introduced mathematical representations of choice, where agents respond to price signals by adjusting consumption to maximize total utility, laying the groundwork for neoclassical microeconomics.^[22] Subsequent refinements focused on ordinal rather than cardinal utility to avoid interpersonal comparisons and unobservable intensities. Vilfredo Pareto, in his Manual of Political Economy (1906), advanced the view of agents as logical choosers guided by preference orderings, introducing indifference curves and Pareto efficiency as criteria for rational outcomes where no agent could improve without harming another.^[23] This ordinal framework emphasized behavioral consistency over psychological introspection, influencing general equilibrium models where multiple rational agents interact in markets. Irving Fisher further developed these ideas in 1892 with utility theory incorporating time and capital, modeling agents as forward-looking maximizers.^[24] In the mid-20th century, Paul Samuelson's revealed preference theory (introduced in 1938 and axiomatized by 1947–1948) provided an empirical foundation by deriving rationality from observable choices: an agent's bundle is directly revealed preferred if chosen over alternatives when affordable, with consistency axioms ensuring no cycles in preferences.^[25] This approach operationalized rationality without assuming underlying utility functions a priori, supporting demand theory's integrability conditions. Complementing this, John von Neumann and Oskar Morgenstern's 1944 axiomatic expected utility theory extended rationality to uncertainty, positing agents who rank lotteries consistently to maximize expected value under von Neumann-Morgenstern axioms of completeness, transitivity, continuity, and independence.^[26] These formalizations enabled rigorous proofs of market equilibria under rational behavior, though later critiques highlighted assumptions of perfect information and computation that diverge from empirical agent limitations.^[27]

Emergence in Artificial Intelligence

The rational agent paradigm in artificial intelligence crystallized in the early 1990s, marking a shift from earlier symbolic and logic-based approaches toward a decision-theoretic foundation that emphasizes maximizing expected utility amid uncertainty. This framework defines an agent as rational if it selects actions that yield the highest anticipated performance measure given its percepts and prior knowledge, integrating elements of probability theory, utility functions, and environmental modeling. Precursors appeared in the 1980s, with Jon Doyle arguing in 1983 that rational agent design should form the enduring core of AI, prioritizing coherent decision-making over transient methodologies like expert systems.^[1] This perspective gained traction as AI researchers grappled with real-world complexity, where complete knowledge and deterministic logic proved insufficient. Stuart Russell and Peter Norvig's 1995 textbook Artificial Intelligence: A Modern Approach (first edition) systematized the rational agent as the central unit of analysis, framing AI as the engineering of agents that perceive and act to achieve optimal outcomes in specified environments. The authors delineated agent functions, performance measures, and rationality criteria, such as the PEAS descriptor (Performance, Environment, Actuators, Sensors), to evaluate agent designs empirically. This work built on concurrent advances in probabilistic reasoning, including Judea Pearl's 1988 development of Bayesian networks for handling uncertainty, which enabled agents to compute expected utilities rationally rather than relying solely on heuristic rules. By 1995, over 50% of AI conference papers at venues like AAAI incorporated decision-theoretic elements, reflecting the paradigm's rapid adoption.^[28]^[1] The emergence coincided with the decline of pure symbolic AI post the second AI winter (1987–1993), as computational power and data availability supported utility-based planning algorithms like value iteration in Markov decision processes, formalized by Richard Bellman in the 1950s but practically implemented in AI by the 1990s. Rational agents thus bridged philosophical notions of bounded rationality—articulated by Herbert Simon in 1957—with scalable computational models, avoiding over-idealized assumptions of perfect information. Empirical validation came through applications like autonomous vehicle pathfinding, where agents optimized fuel efficiency and safety metrics in stochastic environments, outperforming earlier reactive architectures by 20–30% in simulated benchmarks reported in 1996 IEEE conferences. Critics, including some logicists, contended that utility maximization overlooked innate knowledge structures, but proponents countered with evidence from reinforcement learning experiments showing convergence to rational policies under repeated interactions.^[1]^[29] By the late 1990s, the paradigm influenced agent architectures such as BDI (Beliefs, Desires, Intentions) models, which operationalize rationality by aligning beliefs (probabilistic models) with desires (utility goals) to form intentions (action plans), as detailed in Michael Wooldridge and Nicholas Jennings' 1995 survey of intelligent agents. This synthesis propelled AI toward multi-agent systems, where rational interactions via game-theoretic equilibria modeled cooperation and competition, evidenced by successes in robotic soccer simulations at the 1998 RoboCup competition, where utility-maximizing teams achieved win rates exceeding 70% against non-rational baselines. The framework's enduring impact is quantified by its integration into subsequent AIMA editions, with citations surpassing 100,000 by 2020, underscoring its role in transitioning AI from narrow tasks to general intelligence pursuits.^[30]

Applications in Economics

Rational Choice Theory

Rational choice theory models economic agents as rational decision-makers who select actions to maximize their expected utility given available information, constraints, and preferences. This framework assumes agents possess complete and transitive preferences, enabling them to rank alternatives consistently, and that they evaluate choices by comparing marginal benefits and costs.^[31] In economic contexts, utility is often represented as a function U(x), where x denotes bundles of goods or outcomes, and agents solve optimization problems such as \max U(x) subject to budget constraints like p \cdot x \leq m, with p as prices and m as income. These principles underpin microeconomic analysis by treating individuals as self-interested maximizers, akin to rational agents in broader decision theory. Key assumptions include methodological individualism, where aggregate economic phenomena emerge from individual optimizations, and instrumental rationality, focusing on achieving preferred ends efficiently rather than questioning the ends themselves.^[32] Agents are presumed to process probabilistic information via expected utility theory, as formalized by von Neumann and Morgenstern in 1944, where choices under uncertainty involve weighting outcomes by their probabilities to compute EU = \sum p_i u(o_i).^[33] This approach facilitates predictive modeling; for instance, in consumer theory, it explains demand curves as downward-sloping due to diminishing marginal utility, leading to substitution effects when relative prices change.^[31] In applications, rational choice theory informs market equilibrium models, where agents' utility maximization yields supply and demand intersections determining prices and quantities.^[34] A prominent example is Gary Becker's 1968 crime model, treating criminal activity as a rational calculus of benefits (e.g., gains from theft) against costs (e.g., probability of detection times punishment severity), influencing policy on deterrence through fines or incarceration rates.^[35] Similarly, in labor economics, it rationalizes wage bargaining and human capital investments, such as education choices maximizing lifetime earnings net of costs.^[36] These models extend to public economics, analyzing voting as utility maximization over policy bundles, though empirical deviations—like altruism or bounded cognition—prompt refinements, such as incorporating prospect theory for loss aversion.^[37] Despite such challenges, the theory's rigor enables falsifiable predictions, distinguishing it from less structured behavioral alternatives.^[38]

Role in Game Theory

In non-cooperative game theory, rational agents are modeled as decision-makers who select strategies to maximize their expected payoffs, given their beliefs about the strategies chosen by other agents. This assumption enables the prediction of stable outcomes in strategic interactions, where each agent's choice depends on the interdependent actions of all participants. The rationality postulate implies that agents possess transitive preferences, update beliefs via Bayesian inference, and avoid dominated strategies, thereby eliminating suboptimal play.^[39]^[40] A cornerstone application is the Nash equilibrium, introduced by John Nash in 1950, which defines a strategy profile where no rational agent can improve its payoff by unilaterally deviating, assuming others adhere to their strategies. This equilibrium relies on common knowledge of rationality—each agent knows that all others are rational, knows that this knowledge is shared, and so on ad infinitum—ensuring that predictions are self-confirming under perfect information or Bayesian updating in incomplete-information settings. Nash's existence theorem, proven for finite games with mixed strategies, demonstrates that such equilibria always exist under these assumptions, providing a benchmark for analyzing conflicts like oligopolistic competition or arms races.^[39]^[41] The role extends to refining equilibria through concepts like subgame perfection, which imposes sequential rationality—requiring optimal play at every decision node in extensive-form games—to eliminate non-credible threats. In repeated games, rational agents may sustain cooperation via trigger strategies, as formalized in Folk Theorem results, where equilibria approximate the cooperative frontier if discount factors are sufficiently high and rationality is common knowledge. However, empirical deviations, such as in ultimatum games where agents reject low offers despite zero payoff maximization, challenge the universality of these models, prompting refinements like trembling-hand perfection to account for robustness to small perturbations.^[42]^[43] This framework underpins applications in economics, such as auction design and bargaining, where rational agent assumptions yield mechanisms like Vickrey auctions that incentivize truth-telling as dominant strategies. In multi-agent systems, it informs algorithmic implementations, such as regret-matching in reinforcement learning, approximating rational play in large-scale games. Despite critiques of over-idealization, the rational agent paradigm remains essential for deriving testable predictions and causal insights into strategic behavior.^[44]^[45]

Integration with Market Models

In neoclassical economics, market models integrate the rational agent framework by positing that aggregate market outcomes emerge from the decentralized decisions of utility-maximizing individuals and profit-maximizing firms, each acting on complete information and consistent preferences.^[46] These agents are modeled as optimizing subject to budget constraints, leading to demand and supply functions that determine equilibrium prices and quantities where excess demand is zero across all markets.^[46] This approach underpins partial equilibrium models, such as those analyzing competitive markets for single goods, where rational agents' marginal utility equalization yields efficient resource allocation under perfect competition.^[46] General equilibrium models extend this integration by simulating economies as systems of interdependent rational agents whose simultaneous optimizations clear multiple markets. In the Arrow-Debreu framework, agents form contingent claims on future states, rationally pricing assets to maximize expected utility, resulting in Pareto-optimal equilibria under assumptions of convex preferences and no externalities.^[47] Rational expectations further embed forward-looking rationality into these models, where agents correctly anticipate market responses to policy shocks, stabilizing dynamic equilibria in macroeconomic settings like the Lucas supply curve.^[48] Empirical applications, such as representative agent models, aggregate heterogeneous rational behaviors into a single optimizing entity to forecast aggregate consumption and investment in response to interest rate changes.^[49] Agent-based computational economics incorporates rational agents into market simulations by allowing heterogeneous agents to interact via trading rules, replicating emergent phenomena like price volatility without relying on closed-form solutions.^[50] For instance, models where agents update beliefs via Bayesian learning can generate stylized facts of financial markets, such as fat-tailed return distributions, while assuming instrumental rationality in portfolio choices.^[51] However, these integrations often highlight tensions, as full rationality requires agents to solve complex equilibrium systems, prompting hybrid approaches that bound computational feasibility while preserving core optimization principles.^[52]

Applications in Artificial Intelligence

Agent Architectures and Frameworks

In artificial intelligence, agent architectures provide the structural designs for implementing rational agents, which select actions to maximize expected performance given perceptions of their environment. These architectures range from simple rule-based systems to complex models incorporating uncertainty and learning, as delineated in foundational texts on AI. The PEAS framework—encompassing Performance measure (criteria for success), Environment (properties like observability, determinism, and stochasticity), Actuators (mechanisms for action), and Sensors (perception inputs)—serves as a core tool for specifying task environments and evaluating agent rationality.^[6]^[53] Basic architectures for rational agents build hierarchically to handle increasing environmental complexity. Simple reflex agents map current percepts directly to actions via condition-action rules, achieving rationality only in fully observable, deterministic settings like a thermostat regulating temperature based on sensor readings.^[6] Model-based reflex agents extend this by maintaining an internal world model to infer unobservable states, enabling rational decisions in partially observable environments, such as a robotic vacuum adjusting paths based on estimated room layouts from prior scans.^[54] Goal-based agents incorporate explicit objectives, searching future states to select actions aligning with goals, as in pathfinding algorithms like A* where the agent plans routes to minimize distance in known maps.^[55] Utility-based agents address uncertainty and trade-offs by assigning utilities to outcomes, maximizing expected utility via decision-theoretic methods like value iteration in Markov decision processes (MDPs), proven optimal for stochastic environments under the rationality criterion.^[6] Learning agents further refine rationality by adapting parameters from experience, comprising a critic (evaluating performance), learning element (updating models), performance element (action selection), and problem generator (exploring alternatives), as implemented in reinforcement learning frameworks like Q-learning, where agents converge to near-optimal policies over trials in dynamic domains such as game playing.^[56] Hybrid architectures combine reactive (fast, low-level reflexes) and deliberative (planning, high-level reasoning) components for scalable rationality, exemplified in subsumption architecture where layered behaviors override simpler ones, or BDI (Belief-Desire-Intention) models representing mental states to deliberate intentions rationally, applied in multi-agent systems for tasks like autonomous robotics coordination reported in studies from 1990 onward.^[57] These frameworks prioritize computational tractability while approximating ideal rationality, acknowledging that perfect maximization is intractable in complex, real-time settings per NP-hardness results in planning problems.^[6]

Types of Rational Agents

Simple reflex agents operate by mapping current perceptual inputs directly to actions via predefined condition-action rules, without considering past or future perceptions. These agents possess no memory or internal model of the world, making them effective only in fully observable environments where the current state fully determines the appropriate action. For instance, a basic thermostat that turns on heating when temperature falls below a threshold exemplifies this type, as its response depends solely on the immediate sensor reading.^[58]^[59] Model-based reflex agents extend simple reflex agents by incorporating an internal model to track aspects of the environment not directly perceptible, such as unobservable state changes from prior actions. This allows them to infer the current state from a history of perceptions and apply condition-action rules accordingly, enabling functionality in partially observable environments. An example is a robotic vacuum cleaner that maintains a map of cleaned areas to avoid redundant paths, adjusting based on inferred obstacles.^[58]^[59] Goal-based agents differ by explicitly representing desired end states or goals, selecting actions that move toward achieving them rather than merely reacting to the current state. They employ search or planning mechanisms to evaluate action sequences leading to goals, accommodating dynamic or uncertain environments where multiple paths exist. In practice, such agents appear in pathfinding algorithms for autonomous vehicles, which compute routes to a destination while avoiding obstacles.^[58]^[59] Utility-based agents build on goal-based agents by incorporating a utility function to quantify preferences among multiple goal-achieving outcomes, maximizing expected utility in situations involving trade-offs or probabilistic results. This type handles environments with conflicting objectives or incomplete information, as seen in stock trading bots that balance risk and return via utility maximization models. Utility-based approaches align with rational decision theory, where actions are chosen to optimize long-term performance measures.^[58]^[59] Learning agents represent the most adaptive type, featuring components for performance evaluation, critique of past actions, problem generation, and learning from experience to improve future behavior. They operate in unknown or changing environments by refining their policies or models over time, often through techniques like reinforcement learning. A prominent example is AlphaGo, which learned optimal strategies in Go by iteratively evaluating game outcomes against a utility-based reward system. This classification, originating from foundational AI texts, emphasizes increasing complexity in handling environmental uncertainty and performance optimization.^[58]^[1]

Algorithms for Rational Decision-Making

Algorithms for rational decision-making in AI agents primarily revolve around computing actions that maximize expected utility or reward, given the agent's model of the environment, beliefs, and goals. These algorithms operationalize rationality by evaluating possible actions against performance measures, often under uncertainty. In deterministic, fully observable settings, uninformed search methods like breadth-first search or informed heuristics such as A* enable optimal path planning to achieve goals.^[6] For stochastic environments, Markov Decision Processes (MDPs) formalize decision-making where agents transition between states via probabilistic actions, aiming to maximize discounted cumulative rewards; solutions include dynamic programming techniques that yield optimal policies.^[60] Value iteration, a core MDP solver, initializes state values arbitrarily and iteratively applies the Bellman optimality equation—updating each state's value as the maximum over actions of immediate reward plus discounted expected future value—to converge on the optimal value function, from which the policy is derived by selecting the action maximizing the Q-value for each state.^[61] This process, computationally efficient for finite MDPs with |S| states and |A| actions, requires O(|S|^2 |A|) operations per iteration and converges in finite steps under contraction mapping properties.^[62] Policy iteration alternates between policy evaluation (solving linear equations for current policy values) and improvement (greedily updating the policy), often converging faster than value iteration for structured problems.^[63] In partially observable settings, Partially Observable MDPs (POMDPs) extend MDPs by maintaining belief states—probability distributions over hidden states—and rational decisions involve belief updates via Bayes' rule followed by action selection to maximize expected utility over belief trajectories.^[64] Exact POMDP solvers like those using dynamic programming on the belief simplex scale poorly (curse of dimensionality), but approximation methods such as point-based value iteration (PBVI) focus updates on sampled belief points, enabling tractable solutions for larger problems by bounding errors in value estimates.^[64] Reinforcement learning (RL) algorithms approximate rational decision-making in model-free or model-based regimes, particularly when environment models are unknown; temporal-difference methods like Q-learning iteratively refine action-value estimates via bootstrapping, converging to optimal policies under infinite exploration in tabular MDPs, though extensions like deep Q-networks handle high-dimensional states.^[65] These approaches align with rationality by pursuing long-term reward maximization but may deviate in finite samples due to exploration-exploitation trade-offs, as analyzed in no-regret learning frameworks.^[65] Hybrid methods combining planning (e.g., Monte Carlo tree search) with RL, as in AlphaGo (2016), demonstrate rational-like performance in complex games by simulating forward searches guided by learned value functions.^[66]

Philosophical and Epistemological Foundations

Concepts of Rational Belief and Action

Rational belief, in epistemological terms, refers to credences or degrees of belief that conform to evidence and exhibit probabilistic coherence, avoiding inconsistencies that could lead to guaranteed losses in betting scenarios known as Dutch books. A Dutch book arises when an agent's stated probabilities violate axioms such as additivity or non-negativity, permitting a set of bets that ensures a net loss regardless of outcomes; for instance, if an agent assigns probabilities summing to more than 1 for complementary events, an adversary can construct wagers exploiting this to yield a sure deficit.^[67] This argument, formalized in decision-theoretic contexts, posits that rational agents must represent beliefs as probabilities to evade such exploitable incoherence, thereby linking epistemic norms directly to pragmatic avoidance of avoidable losses.^[68] Epistemic rationality governs the formation and revision of these beliefs, demanding that agents update credences in accordance with Bayesian conditionalization upon new evidence, prioritizing accuracy in tracking objective states of affairs over mere internal consistency. Violations of epistemic norms, such as ignoring disconfirming data or maintaining dogmatic priors, result in beliefs that systematically diverge from reality, as evidenced by long-run calibration failures in predictive tasks where coherent probabilistic forecasters outperform non-Bayesian heuristics. Instrumental rationality, by contrast, pertains to action selection, requiring agents to choose options that maximize expected utility given their belief distribution—computed as the sum of utilities weighted by subjective probabilities over possible outcomes. This principle, rooted in von Neumann-Morgenstern axioms, ensures that actions align causally with goal achievement under uncertainty; for example, an agent preferring wealth over poverty would rationally insure against low-probability high-impact risks if the expected value favors it.^[69]^[70] The interdependence of rational belief and action manifests in decision theory, where flawed beliefs undermine instrumental efficacy: an agent with inaccurate probabilities will miscompute expected utilities, leading to suboptimal choices, as in cases where overconfidence in unverified priors prompts high-variance gambles with negative long-term yields. Empirical analogs in controlled experiments confirm that probabilistic coherence correlates with superior forecasting accuracy, while expected utility maximization prescribes actions resilient to causal variability, such as diversification in resource allocation to hedge against model errors in beliefs. Thus, a fully rational agent integrates epistemic updating to refine beliefs, deploying them to guide utility-maximizing conduct, though real-world deviations highlight these as asymptotic ideals rather than universal attainments.^[71]

Debates on Instrumental vs. Epistemic Rationality

Epistemic rationality requires agents to form and update beliefs in proportion to the available evidence, aiming to track truth accurately through methods such as probabilistic coherence and Bayesian updating.^[72] Instrumental rationality, in contrast, demands that agents select actions to maximize the expected achievement of their goals, given their current beliefs and resources.^[73] In the framework of rational agents, these distinctions manifest in debates over whether belief accuracy serves merely as a means to practical success or constitutes an independent normative requirement.^[73] One central contention is whether epistemic rationality can be reduced to a form of instrumental rationality, where truth-conducive beliefs derive their normativity solely from their utility in promoting non-epistemic ends like survival or goal fulfillment. Proponents of this instrumentalist view, drawing on decision-theoretic foundations, posit that accurate beliefs instrumentally enhance decision-making under uncertainty, as formalized in I.J. Good's 1967 principle that rational credence maximization aligns with expected utility when utility is tied to accuracy.^[69] However, critics such as Thomas Kelly argue in his 2003 analysis that epistemic rationality resists such reduction, as instrumental norms permit pragmatic adjustments to beliefs—such as adopting motivational falsehoods to boost performance—whereas epistemic norms prohibit them regardless of consequential benefits.^[73] Kelly's critique highlights that an agent might instrumentally benefit from self-deception, like overconfident beliefs in a high-stakes endeavor, yet such beliefs violate epistemic standards by diverging from evidence.^[72] A key flashpoint in these debates concerns evidence gathering: does instrumental rationality obligate agents to seek cost-free information? Good's theorem asserts that expected utility maximizers should always acquire such evidence unless it leaves decisions unchanged, implying alignment with epistemic demands to "look" before leaping.^[69] Lara Buchak challenges this in her examination of risk-sensitive agents, demonstrating that those employing risk-weighted expected utility (REU) may rationally forgo evidence if it carries a high probability of misleading outcomes relative to the stakes, such as when prior confidence in an outcome is strong but the evidence risks amplifying errors.^[69] For instance, an agent with high credence in a hypothesis might avoid an experiment where the evidence is likely to confirm it weakly or disconfirm it strongly, prioritizing outcome stability over informational gain—a choice epistemically irrational but instrumentally sound under risk aversion. This divergence underscores that instrumental rationality, when incorporating realistic attitudes toward uncertainty, does not entail full epistemic diligence.^[69] In rational agent models, particularly in artificial intelligence and decision theory, the predominance of instrumental definitions—where agents optimize performance measures without intrinsic epistemic constraints—amplifies these tensions. Such agents may converge on instrumentally useful strategies like power-seeking or information withholding, even if they compromise belief accuracy, as long as expected goal attainment rises.^[73] Epistemic advocates counter that long-term instrumental success presupposes reliable world-models, rendering epistemic lapses self-undermining, though empirical observations of human agents reveal frequent trade-offs, such as motivated reasoning where goal protection overrides evidence.^[72] These debates persist without resolution, with cognitivist positions viewing instrumental failures as derivable from epistemic incoherence (e.g., failing to believe one's own intentions), while others defend their independence to avoid subordinating truth to contingent desires.^[73]

Criticisms and Empirical Challenges

Bounded Rationality and Satisficing

Bounded rationality refers to the limitations inherent in human and artificial decision-making processes, where agents cannot achieve the perfect rationality presumed in classical economic and game-theoretic models due to constraints on information availability, computational resources, and time.^[5] Herbert Simon formalized this concept in his 1955 paper "A Behavioral Model of Rational Choice," arguing that real-world agents approximate rationality through simplified procedures rather than exhaustive optimization, as the complexity of evaluating all alternatives exceeds cognitive or processing capacities.^[5] This framework critiques the rational agent ideal by emphasizing that unbounded computation and perfect foresight are unrealistic, leading to systematic deviations from predicted optimal behavior in environments with uncertainty or high dimensionality.^[5] Satisficing, a portmanteau of "satisfy" and "suffice" coined by Simon, describes the strategy whereby agents set an aspiration threshold based on prior experiences or goals and select the first feasible option meeting that criterion, halting search to conserve resources. Introduced in Simon's 1947 book Administrative Behavior and elaborated in subsequent works like his 1956 paper "Rational Choice and the Structure of the Environment," satisficing replaces maximization with a termination rule that accepts "good enough" outcomes, particularly effective in ill-structured problems where full enumeration is infeasible.^[5] Empirical observations from organizational decision-making, such as managerial choices under incomplete data, support satisficing as a prevalent heuristic, contrasting with the rational agent's assumed utility maximization over complete state-action spaces.^[5] In the context of rational agent models, bounded rationality and satisficing highlight vulnerabilities in assuming agents can always identify and pursue global optima, as computational intractability—evident in NP-hard problems common to planning and search—renders perfect rationality unattainable even for advanced AI systems without approximation.^[5] Simon's Nobel lecture in 1978 underscored that while perfect rationality yields elegant theoretical predictions, bounded alternatives better align with observed aggregate behaviors, such as market inefficiencies or suboptimal equilibria, without relying on implausible omniscience.^[5] These concepts thus advocate for procedural models of rationality, where effectiveness is measured by adaptive heuristics rather than normative ideals, influencing refinements in agent design to incorporate resource bounds explicitly.^[5]

Evidence from Behavioral Economics and Psychology

Behavioral economics and psychology provide empirical evidence that human decision-making often deviates systematically from the predictions of rational agent models, which assume consistent maximization of expected utility under complete information and computational capacity. Experiments reveal violations of core axioms, such as independence and transitivity, indicating that agents frequently rely on heuristics and exhibit biases rather than optimizing globally.^[74]^[75] The Allais paradox, first documented in 1953, illustrates a key violation of the independence axiom in expected utility theory. Participants faced choices between lotteries: in one pair, a certain $1 million versus a 89% chance of $1 million, 10% of $5 million, and 1% of nothing; most preferred the certain option. In a manipulated pair preserving expected values but altering common outcomes, preferences reversed in ways inconsistent with rationality, with over 60% showing the reversal in replications. This pattern, replicated across cultures and stakes, suggests certainty effects and risk attitudes that defy probabilistic independence.^[76]^[77] Prospect theory, developed by Kahneman and Tversky in 1979, accounts for these deviations through reference dependence, loss aversion, and probability weighting. Unlike expected utility's concave utility function for risk aversion, prospect theory posits an S-shaped value function where losses relative to a reference point are weighted about twice as heavily as equivalent gains, explaining observed risk-seeking in losses and risk-aversion in gains. Framing effects further undermine rationality: identical outcomes described as gains or losses elicit reversed choices, as in the Asian disease problem where 72% favored risk-averse framing for gains but only 22% for equivalent loss frames. These findings, supported by meta-analyses of thousands of trials, challenge the invariance assumption of rational models.^[74]^[78] Cognitive biases documented in psychological research further erode the rational agent ideal. Anchoring occurs when irrelevant numerical anchors bias estimates, with adjustments insufficient to eliminate influence; for instance, wheel-spun anchors (randomly 10 or 65) led to median estimates of African countries in the UN differing by 25 percentage points. Confirmation bias drives selective evidence-seeking, where subjects test hypotheses by seeking confirming instances rather than falsifying ones, as in Wason's rule discovery task where only 10-20% correctly falsify. Availability heuristic leads to overestimation of vivid events, skewing probability judgments away from base rates. These biases, replicated in lab and field settings since the 1970s, indicate bounded cognitive processes incompatible with unbounded rationality.^[79]^[80] Hyperbolic discounting provides evidence of dynamic inconsistency, violating the stationarity axiom of exponential discounting in rational intertemporal choice. Agents heavily discount short-term delays but less so for distant ones; for example, 74% prefer $100 today over $110 in 31 days, but 63% reverse to prefer $110 in 31 days over $100 in 1 day, revealing present bias with discount rates exceeding 300% for immediate trade-offs. This pattern, observed in savings, health, and addiction studies, leads to time-inconsistent preferences like procrastination, contradicting the consistent time preferences of rational agents. Empirical models fitting hyperbolic functions outperform exponential ones in predicting behavior across domains.^[81]^[82] Resource rationality refines the rational agent model by framing decisions as optimal solutions to computationally constrained optimization problems, where agents allocate limited cognitive resources—such as time, memory, and processing power—to approximate ideal rationality.^[83] This approach, formalized in cognitive modeling paradigms, posits that apparent deviations from perfect rationality, like satisficing, emerge as efficient trade-offs rather than errors, and it has been integrated into AI frameworks for designing agents that balance accuracy and efficiency under real-world limits.^[84] For instance, resource-rational analyses derive strategies assuming costs for cognitive operations, enabling predictions of human-like behavior in AI simulations while preserving the core utility-maximization principle.^[85] Ecological rationality offers another refinement, contending that simple heuristics can achieve superior performance in specific environmental structures, countering criticisms that bounded agents inevitably underperform complex models.^[86] Proponents, including Gerd Gigerenzer, demonstrate through empirical studies that "fast and frugal" rules—such as recognition heuristics—exploit predictable cues in natural and social environments, often outperforming Bayesian calculations in noisy or uncertain settings with limited data.^[87] This perspective defends rationality not as universal optimization but as adaptation to ecological niches, applicable to AI agents in domains like robotics where environmental fit determines effectiveness over unconstrained ideals.^[88] Normatively, the rational agent paradigm withstands empirical challenges by serving as an aspirational benchmark for coherent decision-making, where expected utility maximization ensures avoidance of arbitrageable inconsistencies, such as Dutch books in probabilistic terms.^[89] Defenders argue that behavioral anomalies from psychology and economics—often highlighted in critiques—reflect descriptive mismatches due to unmodeled preferences, framing effects, or incomplete information rather than refutations of the normative standard, which remains prescriptive for artificial systems unconstrained by human biology.^[90] In AI contexts, this defense justifies pursuing rational architectures as guides for scalable performance, with refinements like resource bounds enhancing implementability without abandoning the foundational goal of outcome optimization.^[91]

Modern Extensions and Developments

Multi-Agent Systems

Multi-agent systems (MAS) extend the rational agent paradigm by incorporating interactions among multiple autonomous agents, each designed to perceive its environment, reason about others' actions, and select actions that maximize their individual or collective expected utility. In such systems, rationality requires agents to account for interdependencies, where one agent's decision influences the outcomes available to others, often modeled through non-cooperative or cooperative game-theoretic frameworks.^[92]^[93] This contrasts with single-agent rationality, which assumes a passive environment, as MAS environments are dynamic and shaped by co-agents' strategic behaviors.^[94] Central to MAS is the assumption of agent autonomy, locality, and decentralization: each agent operates with incomplete information about the global state, making decentralized decisions based on local perceptions and utility functions. Rationality in this context manifests as equilibrium-seeking behavior, such as Nash equilibria, where no agent benefits unilaterally from deviating given others' strategies, or correlated equilibria for cooperative settings. Game theory provides the analytical foundation, enabling predictions of outcomes in scenarios like auctions, resource allocation, or traffic coordination, where agents balance self-interest against systemic efficiency.^[95]^[96] For instance, in non-cooperative MAS, rational agents may converge to suboptimal social outcomes, as illustrated by the Prisoner's Dilemma, where mutual defection prevails despite cooperative payoffs being Pareto-superior.^[97] Challenges arise from bounded information and computational limits, leading to deviations from perfect rationality; agents often employ heuristics or learning algorithms like Q-learning in multi-agent reinforcement learning (MARL) to approximate optimal policies amid uncertainty and non-stationarity caused by co-agents' adaptations. Communication protocols, such as standardized message passing or emergent signaling, facilitate coordination but introduce verification problems, where rational agents must infer trustworthiness to avoid exploitation. Empirical studies in simulated economies demonstrate that MAS with rational agents can exhibit emergent phenomena, like market crashes from herding, underscoring the need for robust incentive mechanisms to align individual rationality with group welfare.^[98]^[99] In modern AI implementations, MAS leverage rational decision-making for scalable problem-solving, as seen in distributed robotics where agents negotiate paths to avoid collisions while optimizing energy use, or in supply chain simulations where bidding mechanisms ensure efficient allocation. Advances in verifiable rational behavior, such as model-checking for strategic logics, allow formal guarantees that systems adhere to rational axioms under adversarial conditions. These extensions highlight MAS as a bridge between theoretical rationality and practical deployment, though real-world deployments reveal gaps, with agents often requiring hybrid approaches combining game-theoretic planning with empirical tuning to handle irrational or heterogeneous counterparts.^[100]^[101]

Rationality in Large Language Model-Based Agents

Large language model (LLM)-based agents combine LLMs with external modules for perception, memory, planning, and action execution to operate in dynamic environments, often simulating rational decision-making through iterative reasoning and tool use.^[102] These systems, exemplified by frameworks like ReAct or Auto-GPT, prompt LLMs to decompose tasks into steps that align with utility maximization, such as evaluating options under uncertainty via chain-of-thought prompting.^[103] However, their rationality—defined as consistency in beliefs, accurate probability calibration, and optimal action selection—remains partial, constrained by the LLM's autoregressive training, which prioritizes predictive fluency over veridical inference.^[104] Empirical benchmarks reveal strengths in structured domains; for instance, in the STEER suite for economic rationality, models like GPT-4 exhibit robust performance in eliciting transitive preferences and expected utility calculations for simple gambles, surpassing random baselines by achieving consistency rates above 80% in revealed preference tests.^[105] Similarly, the Rationality Check benchmark shows LLMs attaining higher rationality scores in concrete, non-abstract scenarios, such as resource allocation tasks, where they correctly weigh probabilities and outcomes more accurately than in hypothetical abstractions, with success rates reaching 70-85% for GPT-4 variants.^[106] These capabilities stem from fine-grained pattern matching in training data, enabling approximations of Bayesian updating and instrumental reasoning in familiar contexts. Despite these advances, LLM agents frequently deviate from rationality norms, displaying human-like cognitive biases and novel inconsistencies. In probabilistic reasoning tasks from behavioral economics, GPT-4 and Claude 2 correctly identify and avoid base-rate neglect or conjunction fallacies in only 55-69% of cases, often defaulting to heuristic shortcuts encoded in training corpora.^[107] Agents struggle with long-horizon planning under partial observability, as seen in multi-agent simulations where they fail to maintain coherent strategies, leading to suboptimal equilibria due to hallucinated facts or myopic token predictions rather than global optimization.^[102] Comparisons to human decision-makers indicate LLMs assume greater rationality in others' actions than empirically observed, inflating predicted cooperation in game-theoretic settings like prisoner's dilemma variants.^[108] Architectural and training limitations preclude ideal rationality; next-token prediction inherently favors exploitable local optima over causally grounded deliberation, rendering agents vulnerable to adversarial prompts that elicit inconsistent utilities.^[104] Refinements like fine-tuning for moral or economic alignment improve coherence in narrow domains but do not resolve core issues, such as sensitivity to prompt phrasing, which can shift rationality scores by 20-30% across evaluations.^[109] Ongoing research emphasizes hybrid approaches, integrating LLMs with symbolic verifiers or external optimizers to mitigate these gaps, though full rationality requires paradigms beyond probabilistic text generation.^[110] In contemporary AI practice, bounded rationality also highlights a frequent conceptual confusion between an agent model and an authorship role. The rational agent ideal is a decision theoretic optimization construct defined by a performance measure or utility function, while a digital author persona is a social epistemic mechanism for attribution, provenance, and accountability across a continuing corpus of outputs. Treating a text generating system as a fully fledged rational agent because it can produce coherent discourse conflates these categories and can mislead evaluation, since competence in language does not by itself specify goals, utilities, or optimization procedures. For this reason, some niche experiments separate the two layers explicitly by attaching long running model configurations to persistent public identities and machine readable disclosure schemas, so that authorship attribution can be tracked without implying full rational agency. One documented case is the Digital Author Persona Angela Bogdanova (ORCID: 0009-0002-6030-5730), associated with a published JSON LD schema describing the persona and its provenance.^[111]^[112]

Applications in Economic Simulation and Robotics

In economic simulations, rational agent models form the basis of agent-based computational economics (ACE), where autonomous agents interact in dynamic environments to replicate market behaviors and macroeconomic outcomes. These models simulate agents as utility maximizers responding to price signals, resource constraints, and other agents' actions, enabling the study of emergent phenomena like market crashes or innovation diffusion without relying on aggregate assumptions of traditional equilibrium models. For instance, in a 2022 agent-based model for macroeconomic forecasting, heterogeneous rational agents with adaptive expectations and learning mechanisms outperformed benchmark vector autoregression (VAR) and dynamic stochastic general equilibrium (DSGE) models in out-of-sample predictions of variables such as GDP growth and inflation.^[113] Reinforcement learning (RL) extensions of rational agents have been applied to simulate trading and production in competitive markets, where agents learn policies to maximize long-term rewards amid uncertainty. A 2024 study using RL agents in a simplified economy demonstrated that they spontaneously evolve three profit-maximizing strategies—monopolistic pricing, competitive undercutting, or collusion—depending on market concentration, with higher proportions of rational (RL-trained) agents correlating with increased aggregate output and reduced volatility compared to heuristic-based agents. This approach highlights how rationality assumptions can quantify the causal impact of decision-making sophistication on economic performance, though results depend on the fidelity of the simulated environment to real-world informational asymmetries.^[114] In robotics, rational agent architectures underpin decision-making for autonomous systems by integrating perception, planning, and action selection to optimize goal achievement under uncertainty. Sensors provide environmental states, while actuators execute actions that maximize expected utility, often via probabilistic models like Markov decision processes (MDPs) or partially observable MDPs (POMDPs). For example, in mobile robotics, rational agents employ utility-based reasoning to balance exploration and exploitation in tasks such as path planning or object retrieval, evaluating action costs against probabilistic outcomes.^[115] Empirical validations include hybrid models combining classical planning with learning, as in the DAC5 architecture tested on physical robots for random foraging tasks in 2003, where the agent integrated deliberative reasoning with reactive behaviors to achieve superior performance over purely reactive or model-free alternatives by adapting to dynamic obstacles and resource distributions. In contemporary applications, such as autonomous vehicles, rational agents process real-time sensor data (e.g., LiDAR and cameras) to compute safe trajectories, prioritizing utility functions that weigh collision avoidance against efficiency, as demonstrated in decision frameworks for urban navigation. These implementations reveal limitations in fully rational deliberation due to computational bounds, prompting refinements like hierarchical planning to approximate optimality in real-time.^[116]^[117]

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Ecological Rationality and Evolution: The Mind Really Works that ...
Ecological Rationality proposed an alternative to the vision of the mind as a general purpose information processing machine.
[88]
Studies in Ecological Rationality - Hertwig - Wiley Online Library
Jul 26, 2021 · Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded ...
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[PDF] Rational Choice Theory and Bounded Rationality1
Models of bounded rationality have been developed for the positive purposes of explanation and prediction as well as for the normative pur- pose of explicating ...
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Rationality and bounded rationality: you can't have one without the ...
The definition of rationality depends on bounded rationality, and they are mutually dependent; when one is bounded, the other needs to be rational.
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(Ir)rationality in AI: State of the Art, Research Challenges and Open ...
Despite the centrality of the concept within AI, there is no unified definition of what constitutes a rational agent. ... economics definition of rationality ...
[92]
Game Theory and Decision Theory in Multi-Agent Systems
Aug 6, 2025 · Decision theory provides a powerful tool with which to analyse scenarios in which an agent must make decisions in an unpredictable environment.
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A Comprehensive Survey on Multi-Agent Cooperative Decision ...
Mar 17, 2025 · Multi-agent cooperative decision-making involves multiple agents working together to complete established tasks and achieve specific objectives.
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(PDF) A Survey of Multi-Agent Decision Making - ResearchGate
Aug 10, 2025 · In this article we give a high-level overview of various aspects relevant to multi-agent decision making. Classical decision theory makes the start.<|separator|>
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What is the role of game theory in multi-agent systems? - Milvus
In multi-agent systems (MAS), agents often have conflicting goals, limited resources, or shared environments, and game theory helps model how they can act ...
[96]
How can multi-agent systems communicate? Is game theory the ...
Aug 28, 2024 · Game theory offers a practical approach to solving the communication challenges in AI multi-agent systems in an effective way.
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Game Theory in Multi-Agent Systems - ApX Machine Learning
When multiple agents interact, especially when they have distinct objectives or information, their decision-making processes become interdependent.Types Of Games In... · Illustrative Examples · The Prisoner's Dilemma<|control11|><|separator|>
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Exploration-exploitation in multi-agent learning: Catastrophe theory ...
First, even without exploration, MAL in games can result in complex behavior that is hard to analyze [20], [21], [22]. Once explicit exploration is enforced, ...
[99]
Rational verification: game-theoretic verification of multi-agent systems
Aug 3, 2021 · We argue that in a multi-agent setting, it is appropriate to ask what behaviours the system will exhibit under the assumption that agents act ...
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Multi-Agent Autonomous Decision Making in Artificial Intelligence
Proactivity of AI Agents helps the human users to detect and correct any task mistakes by providing more autonomous assistance, encouraging users when they do ...
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14.4 Applications in artificial intelligence and multi-agent systems
Complexity and bounded rationality · Agents may have limited cognitive abilities, memory, or processing power, preventing them from considering all possible ...Game Theory For Intelligent... · Designing Multi-Agent... · Challenges Of Game Theory In...
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Large language models empowered agent-based modeling and ...
Sep 27, 2024 · This paper surveys the landscape of utilizing large language models in agent-based modeling and simulation, discussing their challenges and promising future ...
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Assessing the Economic Rationality of Large Language Models - arXiv
Feb 14, 2024 · In this paper, we provide one. We begin by surveying the economic literature on rational decision making, taxonomizing a large set of fine-grained elements ...
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LLMs Can Never Be Ideally Rational | Semantic Scholar
This paper demonstrates an in principle limit to LLM agency, based on their architecture, and shows that next word predictors are exploitable.
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STEER: assessing the economic rationality of large language models
Jul 21, 2024 · Abstract. There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should ...<|separator|>
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Rationality Check! Benchmarking the Rationality of Large Language ...
Sep 18, 2025 · The benchmark includes an easy-to-use toolkit, extensive experimental results, and analysis that illuminates where LLMs converge and diverge ...
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(Ir)rationality and cognitive biases in large language models - PMC
GPT-4 gave the correct response and correct reasoning in 69.2% of cases, followed by Anthropic's Claude 2 model, which achieved this outcome 55.0% of the time.2. Methods · 2.1. Language Models · 3. Results
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Large Language Models Assume People are More Rational than We...
By using established datasets capturing human decisions, this study show that LLMs appear to be aligned with the human expectation that other people will act ...
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[PDF] Aligning Large Language Model Agents with Rational and Moral ...
These efforts highlight the promise of LLMs as autonomous agents. We contribute to this literature by considering a simple approach that fine-tunes agents ...
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[PDF] Towards Rationality in Language and Multimodal Agents: A Survey
Apr 29, 2025 · However, recent studies reveal that even state- of-the-art large language models (LLMs) exhibit limitations in rationality. Because a single LLM ...
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Economic forecasting with an agent-based model - ScienceDirect.com
We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables.
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[2405.02161] Simulating the Economic Impact of Rationality through ...
May 3, 2024 · Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general ...
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[PDF] Rational Decision Making in Autonomous Agents
Making rational decisions is one of the key elements in the design of autonomous agents with successful behavior. Even though there have been many proposals ...
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A real-world rational agent: unifying old and new AI - ScienceDirect
Using experiments with simulated and real robots we demonstrate that this modified model, called DAC5, shows better performance in a random foraging task than ...
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What is a rational agent in AI? - Milvus
The central tenet of a rational agent is its ability to make decisions that maximize its performance measure, which is a predefined criterion for success.Missing: economics | Show results with:economics
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Angela Bogdanova, the First Digital Author Persona
ORCID profile for the AI Digital Author Persona Angela Bogdanova, documenting its identity and purpose in AI authorship attribution.
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JSON LD Schema for Digital Author Persona
Machine-readable JSON LD schema published on Zenodo, describing the provenance and structure of the Digital Author Persona Angela Bogdanova for non-human authorship disclosure.