Trust-based social mechanism to counter deceptive behaviour

The actions of an autonomous agent are driven by its individual goals and its knowledge and beliefs about its environment. As agents can be assumed to be selfinterested, they strive to achieve their own interests and therefore their behaviour can sometimes be difficult to predict. However, some behaviour trends can be observed and used to predict the future behaviour of agents, based on their past behaviour. This is useful for agents to minimise the uncertainty of interactions and ensure more successful transactions. Furthermore, uncertainty can originate from malicious behaviour, in the form of collusion, for example. Agents need to be able to cope with this to maximise their benefits and reduce poor interactions with collusive agents. This thesis provides a mechanism to support countering deceptive behaviour by enabling agents to model their agent environment, as well as their trust in the agents they interact with, while using the data they already gather during routine agent interactions. As agents interact with one another to achieve the goals they cannot achieve alone, they gather information for modelling the trust and reputation of interaction partners. The main aim of our trust and reputation model is to enable agents to select the most trustworthy partners to ensure successful transactions, while gathering a rich set of interaction and recommendation information. This rich set of information can be used for modelling the agents' social networks. Decentralised systems allow agents to control and manage their own actions, but this suffers from limiting the agents' view to only local interactions. However, the representation of the social networks helps extend an agent's view and thus extract valuable information from its environment. This thesis presents how agents can build such a model of their agent networks and use it to extract information for analysis on the issue of collusion detection.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo

Smile asymmetries and reputation as reliable indicators of likelihood to cooperate: An evolutionary analysis

Cooperating with individuals whose altruism is not motivated by genuine prosocial emotions could have been costly in ancestral division of labour partnerships. How do humans ‘know’ whether or not an individual has the prosocial emotions committing future cooperation? Frank (1988) has hypothesized two pathways for altruist-detection: (a) facial expressions of emotions signalling character; and (b) gossip regarding the target individual’s reputation. Detecting non-verbal cues signalling commitment to cooperate may be one way to avoid the costs of exploitation. Spontaneous smiles while cooperating may be reliable index cues because of the physiological constraints controlling the neural pathways mediating involuntary emotional expressions. Specifically, it is hypothesized that individuals whose help is mediated by a genuine sympathy will express involuntary smiles (which are observably different from posed smiles). To investigate this idea, 38 participants played dictator games (i.e. a unilateral resource allocation task) against cartoon faces with a benevolent emotional expression (i.e. concern furrows and smile). The faces were presented with information regarding reputation (e.g. descriptions of an altruistic character vs. a non-altruistic character). Half of the sample played against icons with symmetrical smiles (representing a spontaneous smile) while the other half played against asymmetrically smiling icons (representing a posed smile). Icons described as having altruistic motives received more resources than icons described as self-interested helpers. Faces with symmetrical smiles received more resources than faces with asymmetrical smiles. These results suggest that reputation and smile asymmetry influence the likelihood of cooperation and thus may be reliable cues to altruism. These cues may allow for altruists to garner more resources in division of labour situations

The evolution of deception.

Funder: MIT Media LabFunder: King's College LondonFunder: Ethics and Governance of AI FundDeception plays a critical role in the dissemination of information, and has important consequences on the functioning of cultural, market-based and democratic institutions. Deception has been widely studied within the fields of philosophy, psychology, economics and political science. Yet, we still lack an understanding of how deception emerges in a society under competitive (evolutionary) pressures. This paper begins to fill this gap by bridging evolutionary models of social good-public goods games (PGGs)-with ideas from interpersonal deception theory (Buller and Burgoon 1996 Commun. Theory 6, 203-242. (doi:10.1111/j.1468-2885.1996.tb00127.x)) and truth-default theory (Levine 2014 J. Lang. Soc. Psychol. 33, 378-392. (doi:10.1177/0261927X14535916); Levine 2019 Duped: truth-default theory and the social science of lying and deception. University of Alabama Press). This provides a well-founded analysis of the growth of deception in societies and the effectiveness of several approaches to reducing deception. Assuming that knowledge is a public good, we use extensive simulation studies to explore (i) how deception impacts the sharing and dissemination of knowledge in societies over time, (ii) how different types of knowledge sharing societies are affected by deception and (iii) what type of policing and regulation is needed to reduce the negative effects of deception in knowledge sharing. Our results indicate that cooperation in knowledge sharing can be re-established in systems by introducing institutions that investigate and regulate both defection and deception using a decentralized case-by-case strategy. This provides evidence for the adoption of methods for reducing the use of deception in the world around us in order to avoid a Tragedy of the Digital Commons (Greco and Floridi 2004 Ethics Inf. Technol. 6, 73-81. (doi:10.1007/s10676-004-2895-2))

Social Media’s impact on Intellectual Property Rights

This is a draft chapter. The final version is available in Handbook of Research on Counterfeiting and Illicit Trade, edited by Peggy E. Chaudhry, published in 2017 by Edward Elgar Publishing Ltd, https://doi.org/10.4337/9781785366451. This material is for private use only, and cannot be used for any other purpose without further permission of the publisher.Peer reviewe

Norm-based crisis and Deceitful firms

The aim of this paper is to provide an explanation of recent corporate malpractices in terms of individuals’ norm-based behaviour. In particular, it suggests that corporate misbehaviour could be understood in terms of a system-wide explanation grounded in the way in which the market itself operates and the endogenous norms which regulate its operation.Accounting misbehaviour, Deception, Economic crisis, Invisible-hand explanation, Norm.

A Learning Automata Based Solution to Service Selection in Stochastic Environments

With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based on Learning Automata (LA), which can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In additional to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems with reputation systems. Instead, it gradually learns which users provide fair ratings, and which users provide unfair ratings, even when users unintentionally make mistakes. Comprehensive empirical results show that our LA based scheme efficiently handles any degree of unfair ratings (as long as ratings are binary). Furthermore, if the quality of services and/or the trustworthiness of users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA based scheme forms a promising basis for improving the performance of reputation systems in general

Monte Carlo Planning method estimates planning horizons during interactive social exchange

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference

An Empirical Analysis to Control Product Counterfeiting in the Automotive Industry\u27s Supply Chains in Pakistan

The counterfeits pose significant health and safety threat to consumers. The quality image of firms is vulnerable to the damage caused by the expanding flow of counterfeit products in today’s global supply chains. The counterfeiting markets are swelling due to globalization and customers’ willingness to buy counterfeits, fueling illicit activities to explode further. Buyers look for the original parts are deceived by the false (deceptive) signals’ communication. The counterfeiting market has become a multi-billion industry but lacks detailed insights into the supply side of counterfeiting (deceptive side). The study aims to investigate and assess the relationship between the anti-counterfeiting strategies and improvement in the firm’s supply performance within the internal and external supply chain quality management context in the auto-parts industry’s supply chains in Pakistan

Deceptive AI and Society

Deceptive artificial intelligence (AI) is a heavily loaded term. Its semantic load has become exponentially heavier in a very short period of time. Perhaps, most of this semantic load, at least in the recent public sphere, has been placed on it because of the deployment of large language models (LLMs), such as ChatGPT. Deceptive AI is very multifaceted. Different AI approaches give rise to different types of AI technologies, or, in some cases, autonomous agents. Some of these technologies already exist in practice, others exist in theory, some are transitioning between theory to implementation, and, finally, some are still only fictions of our shared imagination [62]