Satisficing is a decision-making strategy or cognitive heuristic that entails searching through the available alternatives until an acceptability threshold is met. The term satisficing, a portmanteau of satisfy and suffice, was introduced by Herbert A. Simon in 1956, although the concept was first posited in his 1947 book Administrative Behavior. Simon used satisficing to explain the behavior of decision makers under circumstances in which an optimal solution cannot be determined. He maintained that many natural problems are characterized by computational intractability or a lack of information, both of which preclude the use of mathematical optimization procedures. He observed in his Nobel Prize in Economics speech that "decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science".
Concept testing is the process of using surveys to evaluate consumer acceptance of a new product idea prior to the introduction of a product to the market. It is important not to confuse concept testing with advertising testing, brand testing and packaging testing, as is sometimes done. Concept testing focuses on the basic product idea, without the embellishments and puffery inherent in advertising.
Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes that make up an individual product or service.
A choice is the range of different things from which one can choose. The arrival at a choice may incorporate motivators and models. For example, a traveler might choose a route for a journey based on the preference of arriving at a given destination at a specified time. The preferred route can then account for information such as the length of each of the possible routes, the amount of fuel in the vehicle, traffic conditions, etc.
Consumer behaviour is the study of individuals, groups, or organizations and all the activities associated with the purchase, use and disposal of goods and services, and how the consumer's emotions, attitudes and preferences affect buying behaviour. Consumer behaviour emerged in the 1940–50s as a distinct sub-discipline of marketing, but has become an interdisciplinary social science that blends elements from psychology, sociology, social anthropology, anthropology, ethnography, marketing and economics.
The analytic hierarchy process (AHP), also analytical hierarchy process, is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. It was developed by Thomas L. Saaty in the 1970s; Saaty partnered with Ernest Forman to develop Expert Choice software in 1983, and AHP has been extensively studied and refined since then. It represents an accurate approach to quantifying the weights of decision criteria. Individual experts’ experiences are utilized to estimate the relative magnitudes of factors through pair-wise comparisons. Each of the respondents compares the relative importance each pair of items using a specially designed questionnaire.
Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory.
Character creation is the process of defining a game character or other character. Typically, a character's individual strengths and weaknesses are represented by a set of statistics. Games with a largely fictional setting may include traits such as race and class. Games with a more contemporary or narrower setting may limit customization to physical and personality traits.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.
In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis. On the other hand, discrete choice analysis examines situations in which the potential outcomes are discrete, such that the optimum is not characterized by standard first-order conditions. Thus, instead of examining “how much” as in problems with continuous choice variables, discrete choice analysis examines “which one.” However, discrete choice analysis can also be used to examine the chosen quantity when only a few distinct quantities must be chosen from, such as the number of vehicles a household chooses to own and the number of minutes of telecommunications service a customer decides to purchase. Techniques such as logistic regression and probit regression can be used for empirical analysis of discrete choice.
EXPRESS is a standard data modeling language for product data. EXPRESS is formalized in the ISO Standard for the Exchange of Product model STEP, and standardized as ISO 10303-11.
Acquiescence bias, also known as agreement bias, is a category of response bias common to survey research in which respondents have a tendency to select a positive response option or indicate a positive connotation disproportionately more frequently. Respondents do so without considering the content of the question or their 'true' preference. Acquiescence is sometimes referred to as "yea-saying" and is the tendency of a respondent to agree with a statement when in doubt. Questions affected by acquiescence bias take the following format: a stimulus in the form of a statement is presented, followed by 'agree/disagree,' 'yes/no' or 'true/false' response options. For example, a respondent might be presented with the statement "gardening makes me feel happy," and would then be expected to select either 'agree' or 'disagree.' Such question formats are favoured by both survey designers and respondents because they are straightforward to produce and respond to. The bias is particularly prevalent in the case of surveys or questionnaires that employ truisms as the stimuli, such as: "It is better to give than to receive" or "Never a lender nor a borrower be". Acquiescence bias can introduce systematic errors that affect the validity of research by confounding attitudes and behaviours with the general tendency to agree, which can result in misguided inference. Research suggests that the proportion of respondents who carry out this behaviour is between 10% and 20%.
All people need to make decisions from time to time. Given limited time in formulating policies and addressing public problems, public administrators must enjoy a certain degree of discretion in planning, revising, and implementing public policies. In other words, they must engage in decision-making. Over the years, many scholars tried to devise decision-making models to account for the policy-making process.
Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices in order to infer positions of the items on some relevant latent scale. Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.
Choice architecture is the design of different ways in which choices can be presented to consumers, and the impact of that presentation on consumer decision-making. For example, each of the following:
Heuristics in judgment and decision-making, simply put, is the process by which humans use mental short cuts to arrive at decisions. Heuristics are simple strategies that humans, animals, organizations and even machines use to quickly form judgments, make decisions, and find solutions to complex problems. Often this involves focusing on the most relevant aspects of a problem or situation to formulate a solution. While heuristic processes are used to find the answers and solutions that are most likely to work or be correct, they are not always right or the most accurate. Judgments and decisions based on heuristics are simply good enough to satisfy a pressing need in situations of uncertainty, where information is incomplete. In that sense they can differ from answers given by logic and probability.
Best–worst scaling (BWS) techniques involve choice modelling and were invented by Jordan Louviere in 1987 while on the faculty at the University of Alberta. In general with BWS, survey respondents are shown a subset of items from a master list and are asked to indicate the best and worst items. The task is repeated a number of times, varying the particular subset of items in a systematic way, typically according to a statistical design. Analysis is typically conducted, as with DCEs more generally, assuming that respondents makes choices according to a random utility model (RUM). RUMs assume that an estimate of how much a respondent prefers item A over item B is provided by how often item A is chosen over item B in repeated choices. Thus, choice frequencies estimate the utilities on the relevant latent scale. BWS essentially aims to provide more choice information at the lower end of this scale without having to ask additional questions that are specific to lower ranked items.
The Programme for the International Assessment of Adult Competencies (PIAAC) is a worldwide study by the Organisation for Economic Co-operation and Development (OECD) in 24 countries of cognitive and workplace skills. The main aim is to be able to assess the skills of literacy, numeracy and problem solving in technology-rich environments, and use the collected information to help countries develop ways to further improve these skills. The focus is on the working-age population. The first data was released on October 8, 2013. A new PIAAC survey is expected to be published in 2021/2022.
DEX is a qualitative multi-criteria decision analysis (MCDA) method for decision making and is implemented in DEXi software. This method was developed by a research team led by Bohanec, Bratko, and Rajkovič. The method supports decision makers in making complex decisions based on multiple, possibly conflicting, attributes. In DEX, all attributes are qualitative and can take values represented by words, such as “low” or “excellent”. Attributes are generally organized in a hierarchy. The evaluation of decision alternatives is carried out by utility functions, which are represented in the form of decision rules. All attributes are assumed to be discrete. Additionally, they can be preferentially ordered, so that a higher ordinal value represents a better preference.
SIMALTO – SImultaneous Multi-Attribute Trade Off – is a survey based statistical technique used in market research that helps determine how people prioritise and value alternative product and/or service options of the attributes that make up individual products or services.
