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#Anticipatory Design #Artificial Intelligence (AI) #Decision Fatigue. #Decision-making #Human-centered AI #Cognitive XD #Human AI Interaction

Anticipatory Design
and Decision Fatigue

How can designers enable anticipation to reduce decision fatigue?

Decision Fatigue
Overview

In 2004, psychologist Barry Schwartz published The Paradox of Choice, a manifesto that outlined the effects of abundance and choice in life. Making decisions requires time and cognitive effort and for Schwartz "the range of choices people face every day has increased in recent years" (2004, 4).

A decade later, Aaron Shapiro, the CEO of digital design agency Huge, developed Schwartz's findings into a new scenario that he called Anticipatory Design [2].

What makes anticipatory design significant is that it is based on the concept of predicting users' actions and needs by helping them to get rid of the abundance of choice. This intrinsically reduces the decision fatigue we face today. This concept emerged as natural cooperation between technology and psychology.

Schwartz [3] defended that too many choices lead to poor quality decisions and less satisfied users. Psychological evidence indicates that the quality of decision declines after an extensive session of decision-making, a phenomenon known as decision fatigue [4].
The paradox of choice by Barry Schwartz - Decision Fatigue and Cognitive XD
Book from Barry Schwartz
The paradox of Choice
Anticipatory design is a premise that explores the potential of AI technology to learn human behavior, to predict their users' choices, and suggest actions or inputs on behalf of them.
Why are businesses chasing transformation to enhance user experience through anticipation experience with the support of AI?

The application of anticipatory design is more important than ever if digital businesses are to simplify and facilitate the course of our digital lives. Modern life has brought us technologies that made our lives more convenient, but it has also subjected us to too many choices. Choices can be good but offering too many choices to the user may have bad consequences such as decision fatigue. "There is evidence that humans are only becoming more indecisive, perhaps as a part of cultural evolution in the information age." [in 5].

Too many choices
decision-making process

The core problem behind today's digital world is that the market is saturated with too many options and choices [3], giving us an abundance of information to inform those choices [2]. For Shapiro "the irony of creating so much choice for ourselves is that — from our health and diet to finances and fitness — people make bad decisions every day. Little ones that add up over time and, sometimes, big ones that ruin their lives." [2]. And the majority of digital products are still in a state of reaction [1], demanding users' attention and inputs all the time, leading them to decision fatigue or analysis paralysis.

The concept studies the effect of the deterioration of the quality of the decisions made by an individual after a long session of decision-making, which leads to decreasing their capability of performance trade-offs [6]. It is also important to infer the user's willingness to avoid using services that can make their decision-making process more exhausting and time-consuming.

Decision fatigue is closely linked to willpower [7]. The authors point out that judging is a hard-mental work, and decision-making depletes our willpower. Once our willpower is depleted, we are less able to move towards a decision. "Part of the resistance against making decisions comes from the fear of giving up options. The more you give up by deciding the more you're afraid of cutting off something vital. This reluctance to give up options becomes more pronounced when willpower is low." [7]. In sum, it takes willpower to make decisions.

Decision-making is a fundamental human process at the center of our interaction with the world [8]. Reasoning and recognition are key poles in decision-making that are closely linked [9]. Reasoning is a distinctly human characteristic of weighing alternatives and selecting a decision based on criteria, while recognition is a stimulus that can result in an action or decision as a learned response without an identifiable reasoning basis [8].

Humans are limited by the information they process. According to Phillips-Wren [8], Herbert A. Simon introduced in 1977 a normative process of decision-making consisting of three phases: (1) Intelligence, (2) Design, (3) Choice; and later in 1997, he introduced one more, (4) Implementation, (see Fig.1).

the process of decision-making
Figure 1: Description of Simon's model of the process of decision-making, retrieved from Phillips-Wren [8].
Research has shown that humans will use strategies such as rationalization, reasoning by analogy, heuristics, trial, and error, local adaptations, avoidance of loss, and control of risk to avoid multi-criteria choices [9]. Such sub-optimal strategies can lead to decision fatigue, which can lead to decision-making cognitive bias errors such as anchoring, status quo, confirming evidence, framing, estimating and forecasting, and sunk cost [10].

Human decision-making, in reality, faces two layers of decision fatigue. One is caused by the current number of options in the market. Apart from decision fatigue, this can also lead users into analysis paralysis [11] and decision avoidance stage [5].

Zuckerberg refers to analysis paralysis as an overload of analysis options that obstructs a person in the decision-making progress [11]. Anderson defines decision avoidance as the manifestation and tendency to avoid making choices by postponing it or by seeking an easy way out that involves no action or no change [5].

Accordingly, users may get frozen in two decision-making loops. In the first, they never leave the analysis stage, they can get stuck in an analysis loop where an analysis is never finished, at Simon's Phase 1 of Intelligence. They keep looking for more details, additional options, more clarifications, making it very hard for them to move forward to the second phase of the decision process. In the second, they never leave a decision analysis stage, at Simon's phase 3 of Choice. They can get stuck in another analysis loop where decision-making is deferred because of requests for more details and analysis. The fear of decision-making is masked by endless requests for more information and options [12].

But even when they leave these loops and move forward in the analysis stage, a digital product can also lead then to decision fatigue by the number of choices and interactions necessary within the service.

Anticipatory Design
a method to streamline the decision-making process

The more choices a designer gives to a user, the longer one will take to reach a decision. It's here that anticipatory and automated design can make a difference by reducing the number of choices and simplifying users' decision-making. Designing for anticipation has the goal of reducing the cognitive overload of users by facilitating their decision-making processes [5, 13–15].

Anticipatory design focuses on this premise and proposes a design method that, through automation, personalizes the user's flow by making and eliminating choice by predicting user experiences and preferences [2, 14].

Fundamentally, what has been established is that interactions will be ruled by time spans like pre-interaction, interaction, and post-interaction. And anticipatory design has the power to reduce users' cognitive overload in the pre-interaction span by giving users what they may want or need before they know they need it. But it can also play an important role in the interaction span by providing them with suggestions for actions based on the solution. And in the post-interaction span by delivering insights or interpret information to the users rather than just display it. Thus, designers can contribute to reducing or even ending decision fatigue, during the entire interaction funnel.
Anticipatory design can streamline the decision-making process in three levels [16].

1. Simplifying selection: With machine learning, algorithms can establish personalized pre-selections defaults and remembering their user's preferences that therefore serve them back. E.g. Netflix.
Anticipatory Design example - Netflix, an Human-centered AI service
Anticipatory Design example - Netflix, an Human-centered AI service
2. Choice editing: Curation plays an important role in choice editing which therefore results in less decision fatigue. By taking away the massive amount of selections and editing the user's choices for them, we can curate all the different information that may streamline a decision for them. This will result in a service optimization for the most common use case. E.g. Easysize.
Anticipatory Design example - Easysize, an Human-centered AI service
Anticipatory Design example - Easysize, an Human-centered AI service
Easysize delivers actionable insights into consumer fashion choices. They are an AI-powered clothing company that developed a recommendation algorithm to accurately decide on behalf of the user which is the best size and fit for them. They are building the world's largest network of consumer style & fit preferences in fashion.
3. Eliminating decisions: With the feature of anticipatory design, designers can design an anticipatory service that surprise and delight users by editing down or eliminating some of the choices that they need to make. This will allow reducing work from the user's shoulders. Nevertheless, designers need to be conscious of how far a service can be designed to adjust and work without conscious human involvement. E.g. Digit.
Anticipatory Design example - Digit.co application, an Human-centered AI service
Anticipatory Design example - Digit.co application, an Human-centered AI service

References
[1] Walorska, A.M.: Turning Data Into Experiences. Pro-active Experiences and their Significance for Customers and Business. Procedia Manuf. 3, 3406–3411 (2015).

[2] Shapiro, A.: The Next Big Thing In Design? Less Choice, https://www.fastcompany.com/3045039/the-next-big-thing-in-design-fewer-choices, last accessed 2020/29/06.

[3] Schwartz, B.: The Paradox of Choice, Why More Is Less. (2004).

[4] Hirshleifer, D., Levi, Y., Lourie, B., Teoh, S.H.: Decision fatigue and heuristic analyst forecasts. J. financ. econ. 133, 83–98 (2019).

[5] Anderson, C.J.: The psychology of doing nothing: Forms of decision avoidance result from reason and emotion. Psychol. Bull. 129, 139–167 (2003).

[6] Baumeister, R.: Irrational Pursuit: Hyper-Incentives from Visceral brain. In: Brocas, I. and Carrillo, J. (eds.) The Psychology of Economic Decisions. Oxford University Press (2002).

[7] Baumeister, R., Tierney, J.: Willpower, Rediscovering the Greatest Human Strengh. , New York (2011).

[8] Phillips-Wren, G.: Intelligent decision support systems. In: Doumpos, M. and Grigoroudis, E. (eds.) Multicriteria Decision Aid and Artificial Intelligence. pp. 25–41. Wiley (2013).

[9] Pomerol, J.-C., Adam, F.A.: Understanding Human Decision Making – A Fundamental Step Towards Effective Intelligent Decision Support. In: Intelligent Decision Making: An AI-Based Approach. pp. 3–40. Yilmaz, Levent Tolk, Andreas (2008).

[10] Wilke, A., Mata, R.: Cognitive bias. Curated Ref. Collect. Neurosci. Biobehav. Psychol. 531–535 (2016).

[11] Zuckerberg, B.: Overcoming analysis paralysis. Front. Ecol. Environ. 6, 505–506 (2008).

[12] Mcsweeney, A.: Stopping Analysis Paralysis And Decision Avoidance In Business Analysis And Solution Design. (2016).

[13] Poli, R.: The many aspects of anticipation. foresight. 12, 7–17 (2010).

[14] Van Bodegraven, J.: How Anticipatory Design Will Challenge Our Relationship with Technology A Future Without Choice. In: The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems. pp. 435–438 (2017).

[15] Zamenopoulos, T., Alexiou, K.: Towards an anticipatory view of design. Des. Stud. 28, 411–436 (2007).

[16] Kleber, S.: How to Get Anticipatory Design Right, https://www.hugeinc.com/articles/how-to-get-anticipatory-design-right, last accessed 2020/29/06.


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