Reputation is a central element of social communications, be it with human or artificial intelligence (AI), and as such can be the primary target of malicious communication strategies. There is already a vast amount of literature on trust networks and their dynamics using Bayesian principles and involving Theory of Mind models. An issue for these simulations can be the amount of information that can be stored and managed using discretizing variables and hard thresholds. Here a novel approach to the way information is updated that accounts for knowledge uncertainty and is closer to reality is proposed. Agents use information compression techniques to capture their complex environment and store it in their finite memories. The loss of information that results from this leads to emergent phenomena, such as echo chambers, self-deception, deception symbiosis, and freezing of group opinions. Various malicious strategies of agents are studied for their impact on group sociology, like sycophancy, egocentricity, pathological lying, and aggressiveness. Our set-up already provides insights into social interactions and can be used to investigate the effects of various communication strategies and find ways to counteract malicious ones. Eventually this work should help to safeguard the design of non-abusive AI systems.

Social communication is omnipresent and a fundamental basis of our daily lives. Especially, due to the increasing popularity of social media, communication flows are becoming more complex, faster and more influential. It is therefore not surprising that in these highly dynamic communication structures, strategies are also developed to spread certain opinions, to deliberately steer discussions or to inject misinformation. The reputation game is an agent-based simulation that uses information theoretical principles to model the effect of such malicious behavior taking reputation dynamics as an example. So far, only small groups of 3 to 5 agents have been studied whereas now we extend the reputation game to larger groups of up to 50 agents also including one-to-many conversations. In this setup, the resulting group dynamics are examined, with particular emphasis on the emerging network topology and the influence of agents' personal characteristics thereon. In the long term the reputation game should thus help to determine relations between the arising communication network structure, the used communication strategies and the recipients' behavior, allowing to identify potentially harmful communication patterns, e.g. in social media.

Reputation is essential to human interactions and shapes group dynamics, however, it can be manipulated. The aim of this paper is to identify key aspects of reputation dynamics that eventually lead to a successful smearing campaign. For this purpose, we introduce an agent-based simulation which captures elements of communication dynamics that make agents more prone to believe lies, and to have an incorrect assessment of the honesty of others. The perceived honesty of agents being regarded here as their reputation, our simulations constitute a reputation game. Similarly to other works in the literature, the use of probability functions and Bayesian logic helps us to capture uncertainties in agents’ beliefs and to model their associated cognitive limitations. However a major novelty of our work with respect to previous simulations on trust and reputation networks is how bounded rationality is dealt with. In our game, agents use information theory to compress all the information available to them in order to capture their complex environment in memories with finite capacity. The imperfect reasoning which results from such a compression process makes our agents vulnerable to deception. We find that this eventually leads to the emergence of communication and behavioral patterns which resemble reality, such as for example echo chambers, self-deception, deception symbiosis, and freezing of group opinions. As a result, the framework we propose could be used to develop methods to mitigate the impact of harmful communication strategies, and in the future safeguard the communication dynamics influenced by artificial intelligence systems in social media.

The electronic surroundings of phosphorus and lithium atoms in the ionic conductor lithium dihydrogen phosphate (LDP) have been studied by single-crystal nuclear magnetic resonance (NMR) spectroscopy at room temperature. From orientation-dependent NMR spectra of a large homegrown LDP single crystal, the full 31 P chemical shift (CS) and 7 Li quadrupole coupling (QC) tensor was determined, using a global fit over three rotation patterns. The resulting CS tensor is characterized by its three eigenvalues: δ PAS 11 = (67.0 ± 0.6) ppm, δ PAS 22 = (13.9 ± 1.5) ppm, and δ PAS 33 = (−78.7 ± 0.9) ppm. All eigenvalues have also been verified by magic-angle spinning NMR on a polycrystalline sample, using Herzfeld-Berger analysis of the rotational side band pattern. The resulting 7 Li QC tensor is characterized by its quadrupolar coupling constant χ = Q PAS 33 = (−71 ± 1) kHz and the two eigenvalues Q PAS 11 = (22.3 ± 0.9) kHz, and Q PAS 22 = (48.4 ± 0.8) kHz. The initially unknown orientation of the mounted crystal, expressed by the orientation of the rotation axis in the orthorhombic crystal frame, was included in the global data fit as well, thus obtaining it from NMR data only.Crystals 2020, 10, 302 2 of 12 with four formula units per unit cell [6]. The PO 4 tetrahedra build a three-dimensional framework connected by two different types of hydrogen bonds. The LiO 4 coordination tetrahedra are linked by their vertices forming [100] isolated chains and share each edge with the PO 4 units. All atoms in the unit cell are located at Wyckoff position 4a with the general site symmetry 1 − C 1 [7]. As a result, no symmetry constraints affect the algebraic form of the NMR interaction tensors, as will be explained in the following.Abstract: Last thing to be done...

Modern communication habits are largely shaped by the extensive use of social media and other online communication platforms. The enormous amount of available data and speed with which new information arises, however, often suffices to cause misunderstandings, false conclusions, or otherwise disturbed opinion formation processes. To investigate some of these effects we use an agent-based model on gossip and reputation dynamics with 50 agents, including Bayesian knowledge updates under bounded rationality and up to the second-order theory of mind effects. Thereby, we observe the occurrence of reputation boosts from fake images, as well as the advantage of hiding one’s opinion in order to become a strong information trader. In addition, the simulations show fundamentally different mechanisms for reaching high agreement with others and becoming well-informed. Additionally, we investigate the robustness of our results with respect to different knowledge-update mechanisms and argue why it makes sense to especially emphasize the margins of distribution when judging a bounded quantity such as honesty in a reputation game simulation.

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