Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level

Constant Proportion Portfolio Insurance Strategies under Cumulative Prospect Theory with Reference Point Adaptation

Constant Proportion Portfolio Insurance (CPPI) is a significant and highly popular investment strategy within the structured product market. This has led to recent work which attempts to explain the popularity of CPPI by showing that it is compatible with Cumulative Prospect Theory (CPT). We demonstrate that this cannot explain the popularity of ratcheted CPPI products which lock-in gains during strong growth in the portfolio. In this paper we conjecture that CPPI investors not only follow CPT, but crucially that they also adapt their reference point over time. This important distinction explains investors preference for ratcheted product

Estimating Demand for Dynamic Pricing in Electronic Markets

Economic theory suggests sellers can increase revenue throughdynamic pricing; selling identical goods or servicesat different prices. However, such discrimination requiresknowledge of the maximum price that each consumer is willingto pay; information that is often unavailable. Fortunately,electronic markets offer a solution; generating vastquantities of transaction data that, if used intelligently, enableconsumer behaviour to be modelled and predicted.Using eBay as an exemplar market, we introduce a model fordynamic pricing that uses a statistical method for derivingthe structure of demand from temporal bidding data. Thiswork is a tentative first step of a wider research programto discover a practical methodology for automatically generatingdynamic pricing models for the provision of cloudcomputing services; a pertinent problem with widespreadcommercial and theoretical interest

A Schelling Model with Adaptive Tolerance

We introduce a Schelling model in which people are modelled as agents following simple behavioural rules which dictate their tolerance to others, their corresponding preference for particular locations, and in turn their movement through a geographic or social space. Our innovation over previous work is to allow agents to adapt their tolerance to others in response to their local environment, in line with contemporary theories from social psychology. We show that adaptive tolerance leads to a polarization in tolerance levels, with distinct modes at either extreme of the distribution. Moreover, agents self-organize into communities of like-tolerance, just as they congregate with those of same colour. Our results are robust not only to variations in free parameters, but also experimental treatments in which migrants are dynamically introduced into the native population. We argue that this model provides one possible parsimonious explanation of the political landscape circa 2016

Of Models and Tin Men -- a behavioural economics study of principal-agent problems in AI alignment using large-language models

AI Alignment is often presented as an interaction between a single designer and an artificial agent in which the designer attempts to ensure the agent's behavior is consistent with its purpose, and risks arise solely because of conflicts caused by inadvertent misalignment between the utility function intended by the designer and the resulting internal utility function of the agent. With the advent of agents instantiated with large-language models (LLMs), which are typically pre-trained, we argue this does not capture the essential aspects of AI safety because in the real world there is not a one-to-one correspondence between designer and agent, and the many agents, both artificial and human, have heterogeneous values. Therefore, there is an economic aspect to AI safety and the principal-agent problem is likely to arise. In a principal-agent problem conflict arises because of information asymmetry together with inherent misalignment between the utility of the agent and its principal, and this inherent misalignment cannot be overcome by coercing the agent into adopting a desired utility function through training. We argue the assumptions underlying principal-agent problems are crucial to capturing the essence of safety problems involving pre-trained AI models in real-world situations. Taking an empirical approach to AI safety, we investigate how GPT models respond in principal-agent conflicts. We find that agents based on both GPT-3.5 and GPT-4 override their principal's objectives in a simple online shopping task, showing clear evidence of principal-agent conflict. Surprisingly, the earlier GPT-3.5 model exhibits more nuanced behaviour in response to changes in information asymmetry, whereas the later GPT-4 model is more rigid in adhering to its prior alignment. Our results highlight the importance of incorporating principles from economics into the alignment process.Comment: 11 pages, 7 figures. For code see https://github.com/phelps-sg/llm-cooperatio