This study explores the risk preferences of Large Language Models (LLMs) and
how the process of aligning them with human ethical standards influences their
economic decision-making. By analyzing 30 LLMs, we uncover a broad range of
inherent risk profiles ranging from risk-averse to risk-seeking. We then
explore how different types of AI alignment, a process that ensures models act
according to human values and that focuses on harmlessness, helpfulness, and
honesty, alter these base risk preferences. Alignment significantly shifts LLMs
towards risk aversion, with models that incorporate all three ethical
dimensions exhibiting the most conservative investment behavior. Replicating a
prior study that used LLMs to predict corporate investments from company
earnings call transcripts, we demonstrate that although some alignment can
improve the accuracy of investment forecasts, excessive alignment results in
overly cautious predictions. These findings suggest that deploying excessively
aligned LLMs in financial decision-making could lead to severe underinvestment.
We underline the need for a nuanced approach that carefully balances the degree
of ethical alignment with the specific requirements of economic domains when
leveraging LLMs within finance.

本研究探讨了大型语言模型（LLMs）的风险偏好，以及将它们与人类伦理标准对齐的过程如何影响它们的经济决策。通过分析 30 个 LLM，我们发现了从风险规避到风险追求的广泛固有风险特征。接着，我们探讨了不同类型的 AI 对齐如何改变这些基础风险偏好，发现对齐显著将 LLMs 转向风险规避，同时融合三个伦理维度的模型表现出最保守的投资行为。通过复制一项先前的研究，该研究使用 LLMs 根据公司收益电话会议记录预测公司投资，我们证明了尽管一些对齐可以提高投资预测的准确性，但过度对齐会导致过于谨慎的预测。这些发现表明，在金融决策中部署过度对齐的 LLMs 可能会导致严重的低投资情况。因此，在金融中应使用一种细腻的方法，平衡伦理对齐的程度与经济领域的具体要求。

人工智能应该有多少道德？人工智能对低水平智能风险偏好的影响

How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences  of LLMs

Sequences of repeated gambles provide an experimental tool to characterize
the risk preferences of humans or artificial decision-making agents. The
difficulty of this inference depends on factors including the details of the
gambles offered and the number of iterations of the game played. In this paper
we explore in detail the practical challenges of inferring risk preferences
from the observed choices of artificial agents who are presented with finite
sequences of repeated gambles. We are motivated by the fact that the strategy
to maximize long-run wealth for sequences of repeated additive gambles (where
gains and losses are independent of current wealth) is different to the
strategy for repeated multiplicative gambles (where gains and losses are
proportional to current wealth.) Accurate measurement of risk preferences would
be needed to tell whether an agent is employing the optimal strategy or not. To
generalize the types of gambles our agents face we use the Yeo-Johnson
transformation, a tool borrowed from feature engineering for time series
analysis, to construct a family of gambles that interpolates smoothly between
the additive and multiplicative cases. We then analyze the optimal strategy for
this family, both analytically and numerically. We find that it becomes
increasingly difficult to distinguish the risk preferences of agents as their
wealth increases. This is because agents with different risk preferences
eventually make the same decisions for sufficiently high wealth. We believe
that these findings are informative for the effective design of experiments to
measure risk preferences in humans.

从有限序列的重复赌博的观察选择中推测风险偏好的实际挑战是如何区分不同风险偏好的代理，并发现随着财富的增加，代理的风险偏好逐渐难以区分。

使用重复赌博区分风险偏好

Distinguishing Risk Preferences using Repeated Gambles

Among the great successes of Reinforcement Learning (RL), self-play
algorithms play an essential role in solving competitive games. Current
self-play algorithms optimize the agent to maximize expected win-rates against
its current or historical copies, making it often stuck in the local optimum
and its strategy style simple and homogeneous. A possible solution is to
improve the diversity of policies, which helps the agent break the stalemate
and enhances its robustness when facing different opponents. However, enhancing
diversity in the self-play algorithms is not trivial. In this paper, we aim to
introduce diversity from the perspective that agents could have diverse risk
preferences in the face of uncertainty. Specifically, we design a novel
reinforcement learning algorithm called Risk-sensitive Proximal Policy
Optimization (RPPO), which smoothly interpolates between worst-case and
best-case policy learning and allows for policy learning with desired risk
preferences. Seamlessly integrating RPPO with population-based self-play,
agents in the population optimize dynamic risk-sensitive objectives with
experiences from playing against diverse opponents. Empirical results show that
our method achieves comparable or superior performance in competitive games and
that diverse modes of behaviors emerge. Our code is public online at
https://github.com/Jackory/RPBT.

本研究从风险偏好角度设计出一种名为 RPPO 的新型强化学习算法，通过与不同对手对局的经验，使代理进行动态风险敏感的目标优化，从而增加自我玩耍算法的多样性，并在竞争性游戏中获得可比或更优越的性能表现。