Utilitarian games such as dictator games to measure fairness have been
studied in the social sciences for decades. These games have given us insight
into not only how humans view fairness but also in what conditions the
frequency of fairness, altruism and greed increase or decrease. While these
games have traditionally been focused on humans, the rise of AI gives us the
ability to study how these models play these games. AI is becoming a constant
in human interaction and examining how these models portray fairness in game
play can give us some insight into how AI makes decisions. Over 101 rounds of
the dictator game, I conclude that AI has a strong sense of fairness that is
dependant of it it deems the person it is playing with as trustworthy, framing
has a strong effect on how much AI gives a recipient when designated the
trustee, and there may be evidence that AI experiences inequality aversion just
as humans.

通过对 101 轮独裁者游戏的研究，我得出结论：人工智能在游戏中表现出强烈的公平意识，这取决于它认为与之互动的人是否值得信任；在指定为受托人时，框架对人工智能给予接收者的数量有很大影响；而且也有证据表明人工智能可能像人类一样对不平等具有厌恶感。

公平提示：人工智能作为游戏玩家

Prompting Fairness: Artificial Intelligence as Game Players

In this study, we investigate the capacity of large language models (LLMs),
specifically GPT-3.5, to operationalise natural language descriptions of
cooperative, competitive, altruistic, and self-interested behavior in social
dilemmas. Our focus is on the iterated Prisoner's Dilemma, a classic example of
a non-zero-sum interaction, but our broader research program encompasses a
range of experimental economics scenarios, including the ultimatum game,
dictator game, and public goods game. Using a within-subject experimental
design, we instantiated LLM-generated agents with various prompts that conveyed
different cooperative and competitive stances. We then assessed the agents'
level of cooperation in the iterated Prisoner's Dilemma, taking into account
their responsiveness to the cooperative or defection actions of their partners.
Our results provide evidence that LLMs can translate natural language
descriptions of altruism and selfishness into appropriate behaviour to some
extent, but exhibit limitations in adapting their behavior based on conditioned
reciprocity. The observed pattern of increased cooperation with defectors and
decreased cooperation with cooperators highlights potential constraints in the
LLM's ability to generalize its knowledge about human behavior in social
dilemmas. We call upon the research community to further explore the factors
contributing to the emergent behavior of LLM-generated agents in a wider array
of social dilemmas, examining the impact of model architecture, training
parameters, and various partner strategies on agent behavior. As more advanced
LLMs like GPT-4 become available, it is crucial to investigate whether they
exhibit similar limitations or are capable of more nuanced cooperative
behaviors, ultimately fostering the development of AI systems that better align
with human values and social norms.

通过实验，本研究发现大型语言模型能够在一定程度上将自然语言描述的利他主义和自私行为转化为适当的行为，但在适应有条件回报的情况下存在局限性，特别是在社会困境的一般人类行为模式方面存在潜在限制。研究呼吁进一步探讨大型语言模型生成的代理在更广泛的社会困境中自动生成行为的因素，探讨模型架构、训练参数和各种合作伙伴策略对代理行为的影响，最终促进更符合人类价值和社会规范的人工智能系统的发展。

利用实验经济学研究大型语言模型中出现的类目标行为

Investigating Emergent Goal-Like Behaviour in Large Language Models  Using Experimental Economics

Many collective decision-making settings feature a strategic tension between
agents acting out of individual self-interest and promoting a common good.
These include wearing face masks during a pandemic, voting, and vaccination.
Networked public goods games capture this tension, with networks encoding
strategic interdependence among agents. Conventional models of public goods
games posit solely individual self-interest as a motivation, even though
altruistic motivations have long been known to play a significant role in
agents' decisions. We introduce a novel extension of public goods games to
account for altruistic motivations by adding a term in the utility function
that incorporates the perceived benefits an agent obtains from the welfare of
others, mediated by an altruism graph. Most importantly, we view altruism not
as immutable, but rather as a lever for promoting the common good. Our central
algorithmic question then revolves around the computational complexity of
modifying the altruism network to achieve desired public goods game investment
profiles. We first show that the problem can be solved using linear programming
when a principal can fractionally modify the altruism network. While the
problem becomes in general intractable if the principal's actions are
all-or-nothing, we exhibit several tractable special cases.

本论文介绍了一个扩展的公共物品博弈模型来考虑利他动机，其中将利他主义通过利他主义图中介的受益纳入效用函数，探索了调节共同利益的杠杆作用和计算复杂性。