Retirement planning, in a financial context, refers to the allocation of finances for retirement. This normally means the setting aside of money or other assets to obtain a steady income at retirement. The goal of retirement planning is to achieve financial independence, so that the need to be gainfully employed is optional rather than a necessity.
The process of retirement planning aims to:
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In recent years, producers such as a financial planner or financial adviser have been available to help clients develop retirement plans, where compensation is either fee-based or commissioned contingent on product sale. Such arrangement is sometimes viewed as conflicting to a consumer's interest to have advice rendered without bias or at cost that justifies value. Consumers can now elect a do it yourself (DIY) approach, given the advent of a large, ever growing body of resources. For example, retirement web-tools in the form of simple calculator, mathematical model or decision support system have appeared with greater frequency. A web-based tool that allows client to fully plan, without human intervention, might be considered a producer. A key motivation beyond the DIY trend is based on many of the same arguments of Lean manufacturing process, a constructive alteration of the relationship between producer and consumer.
Retirement finances touch upon a motley of 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 (computer science) 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 current state description and address uncertainty, volatility, change dynamics (i.e. constancy or determinism is not assumed). Together, these factors raise significant challenges to any current producer claim of model predictability or certainty. Some might even adopt fatalism -- that the full scope of client issues, non-financial included, render the entire problem indeterminate, unsolvable, and meaningless.
The Monte Carlo method is a perhaps the most common form of a mathematical model that is applied to predict long-term investment behavior for a client's retirement planning. Its use helps to identify adequacy of client's investment to attain retirement readiness and to clarify strategic choices and actions. Yet, the investment domain is only financial domain and therefore is incomplete. Depending on client context and despite popular press, the investment domain may have very little importance in relation to a client's other domains - e.g. a client who is predisposed to the use of real estate as primary source of retirement funding.
Contemporary retirement planning models have yet to be validated in the sense that the models purport to project a future that has yet to manifest itself. The criticism with contemporary models are some of the same levied against Neoclassical economics. The critic argues that contemporary models may only have proven validity retrospectively, whereas it is the indeterminate future that needs solution. A more moderate school believes that retirement planning methods must further evolve by adopting a more robust and integrated set of tools from the field of complexity science.