This paper presents a novel approach to optimizing profit margins in non-life
insurance markets through a gradient descent-based method, targeting three key
objectives: 1) maximizing profit margins, 2) ensuring conversion rates, and 3)
enforcing fairness criteria such as demographic parity (DP). Traditional
pricing optimization, which heavily lean on linear and semi definite
programming, encounter challenges in balancing profitability and fairness.
These challenges become especially pronounced in situations that necessitate
continuous rate adjustments and the incorporation of fairness criteria.
Specifically, indirect Ratebook optimization, a widely-used method for new
business price setting, relies on predictor models such as XGBoost or GLMs/GAMs
to estimate on downstream individually optimized prices. However, this strategy
is prone to sequential errors and struggles to effectively manage optimizations
for continuous rate scenarios. In practice, to save time actuaries frequently
opt for optimization within discrete intervals (e.g., range of [-20\%, +20\%]
with fix increments) leading to approximate estimations. Moreover, to
circumvent infeasible solutions they often use relaxed constraints leading to
suboptimal pricing strategies. The reverse-engineered nature of traditional
models complicates the enforcement of fairness and can lead to biased outcomes.
Our method addresses these challenges by employing a direct optimization
strategy in the continuous space of rates and by embedding fairness through an
adversarial predictor model. This innovation not only reduces sequential errors
and simplifies the complexities found in traditional models but also directly
integrates fairness measures into the commercial premium calculation. We
demonstrate improved margin performance and stronger enforcement of fairness
highlighting the critical need to evolve existing pricing strategies.

提出一种基于梯度下降方法的新颖方法，通过优化来最大化盈利率，在非人寿保险市场上实现三个关键目标：1）最大化利润率，2）确保转化率，3）实施公平性准则。

OptiGrad：基于梯度学习的公平高效价格弹性优化

OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a  Gradient Based Learning

The next generation of communication is envisioned to be intelligent
communication, that can replace traditional symbolic communication, where
highly condensed semantic information considering both source and channel will
be extracted and transmitted with high efficiency. The recent popular large
models such as GPT4 and the boosting learning techniques lay a solid foundation
for the intelligent communication, and prompt the practical deployment of it in
the near future. Given the characteristics of "training once and widely use" of
those multimodal large language models, we argue that a pay-as-you-go service
mode will be suitable in this context, referred to as Large Model as a Service
(LMaaS). However, the trading and pricing problem is quite complex with
heterogeneous and dynamic customer environments, making the pricing
optimization problem challenging in seeking on-hand solutions. In this paper,
we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg
game with two steps. In the first step, we optimize the seller's pricing
decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes
the prices of large models iteratively by reasoning customers' future rental
decisions, which is able to achieve a near-optimal pricing solution. In the
second step, we optimize customers' selection decisions by designing a robust
selecting and renting (RSR) algorithm, which is guaranteed to be optimal with
rigorous theoretical proof. Extensive experiments confirm the effectiveness and
robustness of our algorithms.

本文提出了 Large Model as a Service (LMaaS) 的概念，将大型模型的灵活租赁作为智能通信的一种策略，通过优化定价和选择决策来解决动态和异质客户环境下的定价优化问题。

LMaaS：大型模型作为服务的定价策略探索

LMaaS: Exploring Pricing Strategy of Large Model as a Service for  Communication

The article describes the use of deep Q-learning models in the problems of
sales time series analytics. In contrast to supervised machine learning which
is a kind of passive learning using historical data, Q-learning is a kind of
active learning with goal to maximize a reward by optimal sequence of actions.
Model free Q-learning approach for optimal pricing strategies and supply-demand
problems was considered in the work. The main idea of the study is to show that
using deep Q-learning approach in time series analytics, the sequence of
actions can be optimized by maximizing the reward function when the environment
for learning agent interaction can be modeled using the parametric model and in
the case of using the model which is based on the historical data. In the
pricing optimizing case study environment was modeled using sales dependence on
extras price and randomly simulated demand. In the pricing optimizing case
study, the environment was modeled using sales dependence on extra price and
randomly simulated demand. In the supply-demand case study, it was proposed to
use historical demand time series for environment modeling, agent states were
represented by promo actions, previous demand values and weekly seasonality
features. Obtained results show that using deep Q-learning, we can optimize the
decision making process for price optimization and supply-demand problems.
Environment modeling using parametric models and historical data can be used
for the cold start of learning agent. On the next steps, after the cold start,
the trained agent can be used in real business environment.

本文研究了使用深度 Q - 学习模型解决销售时间序列分析问题，发现可以通过使用基于参数模型和历史数据的环境建模来优化决策过程，以实现定价策略和供需问题的优化。