Satisficing in multi-armed bandit problems

Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty. We propose two sets of satisficing objectives for the multi-armed bandit problem, where the objective is to achieve reward-based decision-making performance above a given threshold. We show that these new problems are equivalent to various standard multi-armed bandit problems with maximizing objectives and use the equivalence to find bounds on performance. The different objectives can result in qualitatively different behavior; for example, agents explore their options continually in one case and only a finite number of times in another. For the case of Gaussian rewards we show an additional equivalence between the two sets of satisficing objectives that allows algorithms developed for one set to be applied to the other. We then develop variants of the Upper Credible Limit (UCL) algorithm that solve the problems with satisficing objectives and show that these modified UCL algorithms achieve efficient satisficing performance.Comment: To appear in IEEE Transactions on Automatic Contro

Satisficing in sales competition: experimental evidence

In a duopoly market, aspirations express how much sellers want to earn given their expectations about the other's behavior. We define individually and mutually satisficing sales behavior for given individual beliefs and aspirations. In a first experimental phase, whenever satisficing is not possible, beliefs, aspirations, or sales have to be adapted. In a second phase, testing the absorption of satisficing, participants are free to select nonsatisficing sales profiles. The results reveal that most people are satisficers who, either mandatorily or deliberately, tend to adjust aspiration levels if they cannot be satisfied.

How to think about satisficing

An agent submaximizes with motivation when she aims at the best but chooses a less good option because of a countervailing consideration. An agent satisfices when she rejects the better for the good enough, and does so because the mere good enough gets her what she really wants. Motivated submaximization and satisficing, so construed, are different ways of choosing a suboptimal option, but this difference is easily missed. Putative proponents of satisficing tend to argue only that motivated submaximization can be appropriate while critics of satisficing tend to criticize satisficing, as I construe it. The existing literature, then, leaves satisficing in a very bad state: there are no good arguments for it and there are three unanswered objections to it. This paper clarifies the distinction between motivated submaximization and satisficing and refutes the three most prominent objections to the claim that satisficing can be appropriate

Deontic Pluralism and the Right Amount of Good

Consequentialist views have traditionally taken a maximizing form, requiring agents to bring about the very best outcome that they can. But this maximizing function may be questioned. Satisficing views instead allow agents to bring about any outcome that exceeds a satisfactory threshold or qualifies as “good enough.” Scalar consequentialism, by contrast, eschews moral requirements altogether, instead evaluating acts in purely comparative terms, i.e., as better or worse than their alternatives. After surveying the main considerations for and against each of these three views, I argue that the core insights of each are not (despite appearances) in conflict. Consequentialists should be deontic pluralists and accept a maximizing account of the ought of most reason, a satisficing account of obligation, and a scalar account of the weight of reasons

Using the Theory of Satisficing to Evaluate the Quality of Survey Data

Increasingly colleges and universities use survey results to make decisions, inform research, and shape public opinion. Given the large number of surveys distributed on campuses, can researchers reasonably expect that busy respondents will diligently answer each and every question? Less serious respondents may 'satisfice,' i.e., take short-cuts to conserve effort, in a number of ways-choosing the same response every time, skipping items, rushing through the instrument, or quitting early. In this paper we apply this satisficing framework to demonstrate analytic options for assessing respondents' conscientiousness in giving high fidelity survey answers. Specifically, we operationalize satisficing as a series of measurable behaviors and compute a satisficing index for each survey respondent. Using data from two surveys administered in university contexts, we find that the majority of respondents engaged in satisficing behaviors, that single-item results can be significantly impacted by satisficing, and that scale reliabilities and correlations can be altered by satisficing behaviors. We conclude with a discussion of the importance of identifying satisficers in routine survey analysis in order to verify data quality prior to using results for decision-making, research, or public dissemination of finding