Retirement planning, in a financial context, refers to allocating savings or revenue for retirement. The goal of retirement planning is to achieve financial independence. The process of retirement planning aims to:

• Assess readiness to retire given a desired retirement age and lifestyle, i.e., whether one has enough money to retire
• Identify actions to improve readiness-to-retire
• Acquire financial planning knowledge
• Encourage saving practices

Producers such as financial planners or advisers can help clients develop retirement plans, where compensation is either fee-based or commissioned contingent on a product sale. Such an arrangement is sometimes viewed as conflicting with a consumer’s interest, and the advice rendered cannot be without bias or at a cost that justifies its value. Consumers can now elect a do-it-yourself (DIY) approach. For example, retirement web tools such as a calculator, mathematical model, or decision support system are available online. A web-based tool that thoroughly allows the client to plan without human intervention might be considered a producer. Key motivations of the DIY trend are many of the same arguments for lean manufacturing, a constructive alteration of the relationship between producer and consumer.

Retirement finances touch upon distinct subject areas or financial domains of client importance, including investments (i.e., stocks, bonds, mutual funds); real estate; debt; taxes; cash flow (income and expense) analysis; insurance; defined benefits (e.g., social security, traditional pensions). From an analytic perspective, each domain can be formally characterized and modeled using a different class representation, as defined by a domain’s unique set of attributes and behaviors. Domain models require definition only at a level of abstraction necessary for decision analysis. Since planning is about the future, domains need to extend beyond the current state description and address uncertainty, volatility, and change dynamics (i.e., constancy or determinism is not assumed). Together, these factors raise significant challenges to any current producer’s claim of model predictability or certainty.