arXiv:2602.06357v2 Announce Type: replace Abstract: LLMs can generate a wealth of data, ranging from simulated personas imitating human valuations and preferences, to demand forecasts based on world knowledge. But how well do such LLM-generated distributions support downstream decision-making? For example, when pricing a new product, a firm could prompt an LLM to simulate how much consumers are willing to pay based on a product description, but how useful is the resulting distribution for optimizing the price? We refer to this approach as LLM-SAA, in which an LLM is used to construct an estima
The rapid advancement of LLMs has reached a point where their utility extends beyond text generation to simulating complex human behaviors and preferences for decision-making, prompting research into their effectiveness.
This research explores a novel application of LLMs to generate actionable distributions for strategic decision-making, potentially streamlining market research, forecasting, and optimization in various industries.
Firms can now consider leveraging LLMs to construct simulated distributions for scenarios like product pricing, challenging traditional data collection and analytical methods.
- · Businesses adopting LLM-SAA for market analysis
- · LLM developers and service providers
- · AI researchers focusing on simulation and decision support
- · Traditional market research firms
- · Companies slow to integrate AI into strategic planning
Increased adoption of LLMs for generating simulated market data and consumer insights.
Reduced costs and timeframes for market research and strategic planning, leading to faster product development cycles.
Potential for new economic models where predictive and prescriptive analytics are primarily driven by advanced AI simulations rather than historical data alone.
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Read at arXiv cs.LG