> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/finance-business-and-economics.md). # Finance, business & economics - [Accounting, actuarial, insurance & pension kernels](https://docs.algenta.ai/finance-business-and-economics/accounting-and-actuarial-kernels.md): Depreciation, financial ratios, and time-value-of-money accounting; actuarial mortality, life-expectancy, and reserve math; insurance premium ratemaking and loss-ratio formulas; and pension funding, v - [Auction & market mechanism kernels](https://docs.algenta.ai/finance-business-and-economics/auction-and-market-mechanism-kernels.md): Auction revenue and payment rules (Vickrey/English/Dutch/VCG), closed-form behavioral-economics biases, and voting rules computed directly from raw ballots. - [Macroeconomic indicators & currency kernels](https://docs.algenta.ai/finance-business-and-economics/macroeconomic-indicators-and-currency-kernels.md): Time-value-of-money and loan math, corporate/investment finance ratios, national-economy indicators (GDP, inflation, unemployment), and human population statistics -- four independent formula librarie - [Risk, supply-chain & logistics kernels](https://docs.algenta.ai/finance-business-and-economics/risk-supply-chain-and-logistics-kernels.md): Portfolio/returns-series risk metrics (VaR, Sharpe, drawdown) alongside five independent operations-research modules for demand forecasting, EOQ/safety-stock, procurement economics, logistics KPIs, an - [Business operations & real-estate kernels](https://docs.algenta.ai/finance-business-and-economics/business-operations-and-real-estate-kernels.md): Queueing/inventory/LP-scoring formulas, earned-value and PERT project math, real-estate underwriting ratios and mortgage amortization, and telecom link-budget/traffic-engineering formulas -- four inde - [Collectibles, gaming & sports analytics kernels](https://docs.algenta.ai/finance-business-and-economics/collectibles-gaming-and-sports-analytics-kernels.md): Museum conservation and collections-management arithmetic, coin valuation and grading, lottery combinatorics and expected-value math, and sports performance metrics, across four independent scalar-for --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.algenta.ai/finance-business-and-economics.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.