Beyond the Spreadsheet: How AI, Reinforcement Learning, and Integrated Analytics Are Reshaping Economics and Business

This week's picks span central banking and retail: a Tableau integration that brings analytics into Microsoft 365, a critique of the Bank of England's forecasting models, a BIS transformer built for macroeconomic prediction, and a reinforcement learning algorithm for smarter pricing. Together, they show how AI and integrated analytics are becoming core to how forecasts are made and decisions get done.

1. Insights Wherever You Work: Meet the Tableau App for Microsoft 365. Enable data-driven decisions with an integrated analytics experience across Microsoft Word, PowerPoint, and Teams (Tableau).
2. How to improve the Bank of England’s forecasts. The Bank of England’s forecasts use a suite of models and expert judgements to make predictions about the prospects for Britain’s economy. Their publication attracts huge media attention and are monitored closely by financial markets. But are the Bank’s forecasts as good as they can be? The authors argue no, and offer alternative modelling ideas which could deliver better forecasts (LSE Business Review).
3. Introducing BISTRO: a foundational model for unconditional and conditional forecasting of macroeconomic time series. This article introduces the BIS Time-series Regression Oracle (BISTRO), a general purpose time series model for macroeconomic forecasting. Its edge over traditional econometric approaches lies in its ability to deal with generic unconditional and conditional forecasting tasks, without requiring to adjust the model to the macroeconomic tasks being tackled. Building on the transformer architecture underlying LLMs, BISTRO is fine-tuned on the large repository of macroeconomic data maintained at the BIS. We show that BISTRO provides reliable unconditional forecasts for key macroeconomic aggregates and illustrate how using it for conditional forecasting can help unveiling patterns of nonlinearity in the data (BIS).
4. Intelligent pricing and ordering decisions: A deep multi-agent reinforcement learning algorithm. The dynamic pricing mechanisms of firms and search techniques for historical prices have spurred strategic behaviors and price expectations among customers. These customers predict future markdowns and delay their purchases based on price expectations (i.e., reference prices), where joint pricing and ordering decisions should be considered carefully to counteract or soften the negative impact of customers’ strategic behaviors and their reference prices on the seller’s profitability. Moreover, relevant literature neglects general and important features in the actual market (e.g., nonzero price thresholds and randomness in the formation of reference prices). In this study, we establish a Markov game between the retailer and strategic customers over a multi-period horizon, and set the reference price as a three-regime linear piecewise model with loss and gain thresholds. Then, we propose an adaptive multi-agent deep deterministic policy gradient (MA2DDPG) algorithm containing the experience replay buffer separation mechanism and delayed actor updates. Experimental results with synthetic and real-world datasets reveal that our algorithm converges to optimal policies in terms of convergence and rationality, and that it vastly outperforms the benchmark algorithms. Moreover, valuable managerial insights are obtained through an extensive numerical analysis for practitioners, particularly when they must consider the complicated characteristics of heterogeneous customer populations, such as disappointment behaviors, price thresholds, and strategic customer proportions. Our results pave the way for future research aimed at using multi-agent reinforcement learning to improve operations management with behavioral factors (Journal of Management Science and Engineering).

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