Agent's Beliefs Research Articles

Fragile Mentalizing: Lack of Behavioral and Neural Markers of Social Cognition in an Established Social Perspective Taking Task when Combined with Stress Induction.

The growing field of social neuroscience is reliant on the development of robust, ecologically valid paradigms for simulating social interaction and measuring social cognition in highly controlled laboratory settings. Perspective taking is a key component of social cognition, and accordingly several paradigms aimed at measuring perspective taking exist. A relatively novel paradigm is the ball detection task, in which participants and a virtual agent form independent beliefs about the presence of a target stimulus behind an occluder. Previous studies have shown that incongruent trials (in which the participant's and the agent's beliefs differ) affect participant reaction times and elicit increased neural activity in the so-called mentalizing network. This paradigm has important advantages over previous ones, in that experimental conditions can be fully randomized, and ceiling effects are not found even for adult populations. Here, we combined this paradigm with a stress induction and a nonstressful control task. In an online study, we found no evidence of perspective taking at the behavioral level. Combining the task with functional magnetic resonance imaging, we found no evidence of perspective taking at the behavioral or neural level, even for the control condition. While this paradigm is reliable on its own, implementing it in the context of a task-switching paradigm appears to reduce participants' focus on task-irrelevant perspective taking elements. Our findings highlight the fragility of existing social cognition paradigms and the need for reliable, simple, and ecologically valid measures of perspective taking.

Fisher Meets Bayes: The Value of Randomisation for Bayesian Inference of Causal Effects

SummaryFor a Bayesian agent with beliefs about the relationship between covariates and potential outcomes, deterministically selecting an assignment that yields optimal covariate balance rationally dominates randomisation. However, randomisation—by enabling control over the probabilities of erroneous causal conclusions due to unknown covariate imbalances—offers insurance against the possibility that an agent's beliefs may be misleading. For the most part, such rational justifications for optimum assignment have presupposed the framework of Bayesian inference, while such epistemic justifications for randomisation have presupposed the framework of significance testing. In this paper, I build on a conception of balance that seems inextricable from the significance testing framework, Fisherian balance, to show that it implies an analogous epistemic justification for randomisation within the framework of Bayesian inference. Consequently, for the choice between optimum and random assignment, this paper shows that epistemic justifications need not be wedded to significance testing nor must Bayesian inference be wedded to rational justifications.

Opinion dynamics beyond social influence

Abstract We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.

Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments.

Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called "conflict-blocking", where agents' conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.

Structural encroachment

AbstractMoral encroachment states that moral factors can make a difference to what we are epistemically justified in believing. I present two motivating cases that resemble a common example in the moral encroachment literature to show that the agent's commitments and beliefs, and not the moral factors of the situation, influence epistemic justification. I call this view Structural Encroachment.

How Partisanship Can Moderate the Influence of Communicated Information on the Beliefs of Agents Aiming to Form True Beliefs

ABSTRACT Partisan epistemology – individuals granting greater credibility to co-partisan sources in evaluating information – is often taken to be evidence of directionally motivated reasoning in which concerns about group membership override concerns about accuracy. Against this dominant view, I outline a novel accuracy-based account of this mode of reasoning. According to this account, partisan epistemology stems from the inference that co-partisans are more likely to be right as they have superior epistemic access to the relevant facts and seek to realize the correct values. I argue that this theory fits better with relevant findings than motivated-reasoning theories of partisan epistemology. Finally, I suggest it has adequate explanatory power vis-à-vis patterns of misinformation belief.

Statistical uncertainty and coarse contracts

This paper studies a robust contracting problem with a standard principal and a strictly risk-averse agent who is (i) uncertain about the production technology and (ii) ambiguity-averse in the sense of the Bewley (1986) incomplete preferences criterion. When the agent's uncertainty is sufficiently limited, the optimal contract is fully contingent on the state of the world, as in the classical problem. Conversely, when that uncertainty is sufficiently extensive, the optimal contract is (generically) a binary contract with only two distinct payment levels. In intermediate cases, the optimal contract becomes progressively more detailed as the agent becomes increasingly certain about the technology. We provide a statistical interpretation of our model under which the principal's beliefs and the agent's belief sets are as if they were derived from the public observation of i.i.d. output data.

Trusting: Alone and together

ABSTRACT We study the problem of an agent continuously faced with the decision of placing or not placing trust in an institution. The agent makes use of Bayesian learning in order to estimate the institution’s true trustworthiness and makes the decision to place trust based on myopic rationality. Using elements from random walk theory, we explicitly derive the probability that such an agent ceases placing trust at some point in the relationship, as well as the expected time spent placing trust conditioned on their discontinuation thereof. We then continue by modeling two truster agents, each in their own relationship to the institution. We consider two natural models of communication between them. In the first (“observable rewards”) agents disclose their experiences with the institution with one another, while in the second (“observable actions”) agents merely witness the actions of their neighbor, i.e. placing or not placing trust. Under the same assumptions as in the single agent case, we describe the evolution of the beliefs of agents under these two different communication models. Both the probability of ceasing to place trust and the expected time in the system elude explicit expressions, despite there being only two agents. We therefore conduct a simulation study in order to compare the effect of the different kinds of communication on the trust dynamics. We find that a pair of agents in both communication models has a greater chance of learning the true trustworthiness of an institution than a single agent. Communication between agents promotes the formation of long-term trust with a trustworthy institution as well as the timely exit from a trust relationship with an untrustworthy institution. Contrary to what one might expect, we find that having less information (observing each other’s actions instead of experiences) can sometimes be beneficial to the agents.

Manufacturing the Illusion of Epistemic Trustworthiness

Abstract There are epistemic manipulators in the world. These people are actively attempting to sacrifice epistemic goods for personal gain. In doing so, manipulators have led many competent epistemic agents into believing contrarian theories that go against well-established knowledge. In this paper, I explore one mechanism by which manipulators get epistemic agents to believe contrarian theories. I do so by looking at a prominent empirical model of trustworthiness. This model identifies three major factors that epistemic agents look for when trying to determine who is trustworthy. These are (i) ability, (ii) benevolence, and (iii) moral integrity. I then show how manipulators can manufacture the illusion that they possess these factors. This leads epistemic agents to view manipulators as trustworthy sources of information. Additionally, I argue that fact-checking will be an ineffective – or even harmful – practice when correcting the beliefs of epistemic agents who have been tricked by this illusion of epistemic trustworthiness. I suggest that in such cases we should use an alternative correction, which I call trust undercutting.

Dynamic Programming Principle for Automatic Negotiations

In automatic negotiation, an automated agent is trained to negotiate on behalf of a human negotiator. We consider that the domain of negotiations is known to both the agent and its opponents. In this setting, a greedy concession algorithm (GCA) is employed to find an optimal policy for the agent when the agent's belief about the opponent is known. However, the GCA is computationally expensive to certain class of policies. In this article, we propose a reverse greedy concession algorithm that is computationally less expensive for such class of policies. We also provide an alternate proof to establish that the GCA is a dynamic programming principle.

The worldview influence on the internal beliefs of law enforcement agents in the context of exercising their discretionary powers

Despite many years of attempts by scholars and practitioners to determine the reason for the extremely low level of public trust in the judiciary and law enforcement agencies in Ukraine and ways to strengthen it, this level is still critical, and therefore there is a need to identify new ways to overcome such a crisis. Seeing such public distrust in the legal right of procedural decision-makers to make decisions based on their internal conviction, the study aims to review and analyse the scientific developments in the field of procedural decision-making by law enforcement agencies based on their internal conviction and to identify the factors which influence such conviction. Analysis, synthesis, and generalisation methods were used in the study of the outlined issues, which were used to process the bibliography of the issues, whereas deductive logical analysis, inductive generalisation and analogy were used to substantiate the results and formulate the conclusions of the study. Based on the study results, it is established that scholars have studied the concepts of worldview and internal conviction of procedural decision-makers separately. It is proved that any internal conviction is based primarily on a personʼs values and worldview orientations which are formed throughout life, primarily in childhood and adolescence. Regardless of the position held by a person, when faced with a problem in professional activity on which the law enforcement entity has already formed an opinion, the latter may make a procedural decision contrary to the actual circumstances of the case and the evidence available in it. The practical significance of this study is that the results obtained may become the basis for changes in the current legislation on the assessment of candidates for positions with discretionary powers, which involves focusing on their value beliefs and worldview

Moral Encroachment under Moral Uncertainty

This paper discusses a novel problem at the intersection of ethics and epistemology: there can be cases in which moral considerations seem to "encroach'' upon belief from multiple directions at once, and possibly to varying degrees, thereby leaving their overall effect on belief unclear. We introduce these cases -- cases of moral encroachment under moral uncertainty -- and show that they pose a problem for all predominant accounts of moral encroachment. We then address the problem, by developing a modular Bayesian framework that, we argue, is sufficiently flexible and scaleable to accommodate the multifaceted uncertainty we describe while still generating clear recommendations for an agent's beliefs. Our framework has several practical upshots, and we close by articulating them: we derive insights about the relationship between moral character and doxastic behavior and make suggestions for how to encourage people to revise their doxastic states in morally laudable ways, without deviating from core Bayesian norms.

Epistemic control without voluntarism

AbstractIt is tempting to think (though many deny) that epistemic agents exercise a distinctive kind of control over their belief‐like attitudes. My aim here is to sketch a “bottom‐up” model of epistemic agency, one that draws on an analogous model of practical agency, according to which an agent's conditional beliefs are reasons‐responsive planning states that initiate and sustain mental behavior so as to render controlled.

Toward credible belief base revision

This paper deals with belief base revision, a form of belief change which consists in restoring consistency with the intention of incorporating a new piece of information from the environment, while minimally modifying the agent's belief state represented by a finite set of propositional formulas. In an effort to guarantee more reliability and rationality for real applications while performing revision, we come up with the idea of credible belief base revision. We define two new formula-based revision operators using tools offered by evidence theory. These operators, uniformly presented in the same spirit as [92,13], stem from consistent sub-bases maximal with respect to credibility instead of set inclusion or cardinality. Logical properties and productivity of inference from these operators are established, along with complexity results.

Deploying OWL ontologies for semantic mediation of mixed-reality interactions for human–robot collaborative assembly

For effective human–robot collaborative assembly, it is paramount to view both robots and humans as autonomous entities in that they can communicate, undertake different roles, and not be bound to pre-planned routines and task sequences. However, with very few exceptions, most of recent research assumes static pre-defined roles during collaboration with centralised architectures devoid of runtime communication that can influence task responsibility and execution. Furthermore, from an information system standpoint, they lack the self-organisation needed to cope with today’s manufacturing landscape that is characterised by product variants. Therefore, this study presents collaborative agents for manufacturing ontology (CAMO), which is an information model based on description logic that maintains a self-organising team network between collaborating human–robot multi-agent system (MAS). CAMO is implemented using the Web Ontology Language (OWL). It models popular notions of net systems and represents the agent, manufacturing, and interaction contexts that accommodate generalisability to different assemblies and agent capabilities. As a novel element, a dynamic consensus-driven collaboration based on parametric validation of semantic representations of agent capabilities via runtime dynamic communication is presented. CAMO is instantiated as agent beliefs in a framework that benefits from real-time dynamic communication with the assembly design environment and incorporates a mixed-reality environment for use by the operator. The employment of web technologies to project scalable notions of intentions via mixed reality is discussed for its novelty from a technology standpoint and as an intention projection mechanism. A case study with a real diesel engine assembly provides appreciable results and demonstrates the feasibility of CAMO and the framework.

Markovian persuasion with two states

This paper addresses the question of how to best communicate information over time in order to influence an agent's belief and induced actions in a model with a binary state of the world that evolves according to a Markov process, and with a finite number of actions. We characterize the sender's optimal message strategy in the limit, as the length of each period decreases to zero. We show that the limit optimal strategy is myopic for beliefs smaller than the invariant distribution of the underlying Markov process. For beliefs larger than the invariant distribution, the optimal policy is more elaborate and involves both silence and splitting of the receiver's beliefs; it is not myopic.

Pragmatic encroachment and justified group belief

The theory of pragmatic encroachment states that the risks associated with being wrong, or the practical stakes, can make a difference to whether one’s evidence is good enough to justify belief. While still far from the orthodox view, it has garnered enough popularity that it is worth exploring the implications when we apply the theory of pragmatic encroachment to group epistemology, specifically to the justificatory status of the beliefs of group agents. When we do, I claim, we discover two novel cases of divergence; cases where a group epistemic agent is justified in believing but none of the members are, and vice versa. Using Jennifer Lackey’s influential Group Epistemic Agent Account as a foil, in particular Lackey’s arguments against previous proposed cases of divergence, the present paper defends the following argument, which I call Pragmatic Encroachment Divergence (PED): (i) Practical stakes make a difference to what an agent (group or individual) is justified in believing. (ii) The practical stakes of a group agent can come apart from the practical stakes of the (operative) members. (iii) Therefore, it is possible for the justified beliefs of a group to diverge from the justified beliefs of its members.

Acting on belief functions

The degrees of belief of rational agents should be guided by the evidence available to them. This paper takes as a starting point the view—argued elsewhere—that the formal model best able to capture this idea is one that represents degrees of belief using Dempster–Shafer belief functions. However, degrees of belief should not only respect evidence; they also guide decision and action. Whatever formal model of degrees of belief we adopt, we need a decision theory that works with it: that takes as input degrees of belief so represented. The task of this paper is to develop such a decision theory for the belief function model of degrees of belief. This is not the first paper to attempt that task, but compared to the existing literature it takes a more abstract route to its destination, via a consideration of the very idea of rational decision making in light of one’s beliefs and desires. After presenting the new decision theory and comparing it to existing views, the paper goes on to consider diachronic decision situations.

Mindreading by body: incorporating mediolateral balance and mouse-tracking measures to examine the motor basis of adults' false-belief tracking.

The role played by motor representations in tracking others' belief-based actions remains unclear. In experiment 1, the dynamics of adults' anticipatory mediolateral motor activity (leftwards-rightwards leaning on a balance board) as well as hand trajectories were measured as they attempted to help an agent who had a true or false belief about an object's location. Participants' leaning was influenced by the agent's belief about the target's location when the agent was free to act but not when she was motorically constrained. However, the hand trajectories participants produced to provide a response were not modulated by the other person's beliefs. Therefore, we designed a simplified second experiment in which participants were instructed to click as fast as possible on the location of a target object. In experiment 2, mouse-movements deviated from an ideal direct path to the object location, with trajectories that were influenced by the location in which the agent falsely believed the object to be located. These experiments highlight that information about an agent's false-belief can be mapped onto the motor system of a passive observer, and that there are situations in which the motor system plays an important role in accurate belief-tracking.

Situated conditional reasoning

Conditionals are useful for modelling many forms of everyday human reasoning but are not always sufficiently expressive to represent the information we want to reason about. In this paper, we make a case for a form of situated conditional. By ‘situated’, we mean that there is a context, based on an agent's beliefs and expectations, that works as background information in evaluating a conditional, and we allow such a context to vary. These conditionals are able to distinguish, for example, between expectations and counterfactuals. Formally, they are shown to generalise the conditional setting in the style of Kraus, Lehmann, and Magidor. We show that situated conditionals can be described in terms of a set of rationality postulates. We then propose an intuitive semantics for these conditionals and present a representation result which shows that our semantic construction corresponds exactly to the description in terms of postulates. With the semantics in place, we define a form of entailment for situated conditional knowledge bases, which we refer to as minimal closure. Finally, we proceed to show that it is possible to reduce the computation of minimal closure to a series of propositional entailment and satisfiability checks. While this is also the case for rational closure, it is somewhat surprising that the result carries over to minimal closure.