What happens when we have a reliance on intuition when analytical reasoning is needed? How we form judgments and make decisions is fundamental to life’s outcomes. Dual-system theory suggests we use two main cognitive systems. One is responsible for our intuitive processing, which is relatively fast, automatic, and effortless. The second is responsible for our conscious reasoning, which is relatively slow, controlled, and effortful. A third perception system is responsible for our ability to perceive input. Distinct from our two other cognitive systems, it provides information fed into System 1 to form intuitive impressions. Then, these impressions undergo deliberate operations of reasoning by System 2 to form conscious judgments that underpin decisions.
Perception is a process of observation and interpretation.
An important concept for helping us understand our interpretations is bias. Bias is the unequal assessment between two alternatives, which typically puts one option in a favorable position and the other unfavorably. Unconscious biases are everywhere. From the neighborhood that we choose, our close friends, and the people we date.
Developments in neuroscience now demonstrate that many biases are formed throughout life and held at the subconscious level, mainly through societal and parental conditioning. We gather millions of bits of information, and our brain processes that information in a certain way – unconsciously categorizing and formatting it into familiar patterns. Though most of us have difficulty accepting or acknowledging it, we all do it. Gender, ethnicity, disability, sexuality, body size, profession, politics, etc, All influence the assessments that we make of people and form the basis of our relationship with others and the world in general.
The great irony of unconscious bias is that most of us have a bias, and it is our belief that we are not affected by bias, a phenomenon known as the bias blind spot. There are around 100 unconscious biases.
Cognitive bias
The egocentric bias is a cognitive bias that causes people to rely too heavily on their point of view when they examine events in their lives or try to see things from another perspective. Accordingly, the egocentric bias causes people to either underestimate how different another’s viewpoint is from their own or to ignore another’s viewpoint entirely. Examples of cognitive biases appear in specific domains. For instance:
- Cognitive biases can cause entrepreneurs to be overly confident and optimistic.
- When it comes to medicine, cognitive biases may cause doctors to misdiagnose patients displaying atypical symptoms.
In all aspects of life; When we hire, when we make decisions.
Many explanations of cognitive biases are grounded in dual-system theory, suggesting we use two main cognitive systems.
- System 1. This system is responsible for our intuitive processing, which is relatively fast, automatic, and effortless. Accordingly, processes on this system run in parallel, meaning that it’s possible to engage this system on multiple fronts simultaneously. This system tends to be relatively strongly influenced by emotions. An example of a situation where System 1 is engaged is when we feel pleased because someone laughed at a joke that we told.
- System 2. This system is responsible for our conscious reasoning, which is relatively slow, controlled, and effortful. Accordingly, processes in this system run serially, meaning that this system can only focus on one thing at a time. This system tends to be relatively detached from emotional considerations. An example of a situation where System 2 is engaged is when we attempt to solve a complex mathematical equation.
Under this framework, a common cause of cognitive biases is relying on intuition (System 1) when analytical reasoning (System 2) is needed. This can happen because intuition is relatively fast and easy to use and can lead to outcomes that are as good as or better than analytical reasoning in many cases. Hence, people often rely on it even when it’s not appropriate. Our perception system, which is responsible for perceiving input, is distinct from our two other cognitive systems (System 1 and System 2). Though there are some similarities between our perception system and System 1 since they both consist primarily of automatic processes that run in parallel to each other, there are some key differences between them:
- Our perception is relatively neutral, while System 1 is influenced by various emotional considerations.
- Our perception is evoked by direct stimulus only, while System 1 can be evoked by things such as thoughts.
- Our perception is limited to perceptual representations, while System 1 can generate abstract representations.
- Our perception is limited to stimuli we encounter in the present, while System 1 can also deal with information related to the past or the future.
Our perceptual system provides information fed into System 1 to form intuitive impressions. Then, these impressions undergo deliberate operations of reasoning by System 2 to form conscious judgments that underpin decisions.
Personally or professionally, in each decision, we might ignore or ingest data (trusted or not) or miss unavailable relevant data, making instinctual judgments through our own bias lens that might be correct or not.
Business decisions are based on the judgments of individual employees, teams, managers, and leadership collectively. A company’s values and culture, organizational framework, and operating system, including policies, processes, procedures, management, governance, help to provide decision-making guardrails. Successful companies make decisions, act, and learn from them.
Data-enabled decision making
An elevated challenge in a digital economy is the sheer volume of information. There are vast amounts of accessible, contextually valuable business data but too much for human system 2 – conscious reasoning – to manage. While 1, 2, or even three data dimensions are easy for humans to understand and learn, a machine can learn in many more dimensions.
In the last decade, advances in technology and computing power have led to the rapid evolution of data science and AI. Data science is an umbrella term to describe the entire complex and multistep processes used to extract value from data. Machines can look at lots of high dimensions of data and determine patterns. From patterns, they can discover anomalies, spot trends, and generate predictions based on learning with increasing probability – Bayes probability theorem and the “geometry of changing beliefs”. Once patterns are learned, predictions can be made that humans can’t even come close to. This is machine learning and part of the world of AI in which data scientists extract insights from data to solve complex challenges. With data analytics capabilities companies can draw calculated insights to arrive at conscious judgments that humans are unable to derive on their own.
It’s a Digital World
With 4.388 bn internet users, 5.112 bn mobile users & 3.484 bn active social media users worldwide Global Digital Reports 2020); In 2021, 2.5 quintillion bytes of data are created every day. 90% of the world’s data has been generated alone over the last two years. A staggering figure, it is expected that the volume of data is to double every two years.
When large amounts of information are widely accessible and circulated, the ability to ingest data, analyze, reason, and act has become instantaneous. While many companies lean on metrics to measure performance and help steer the business, they miss a virtual untapped gold mine of other data. Hidden in the data are insights into a business, clients, competitors, and markets.
Companies with analytical capabilities drive data-enabled decision-making, augmenting the ability to choose wisely, act intentionally, lessening reliance on gut instinct and intuition. Data analytics underpin an organization’s ability to delight customers, automate mundane tasks, reduce costs, increase efficiencies, engage and empower a workforce. Without harnessing data, companies cannot appropriately lean on system 2 and may rely too much on system 1.
Leaders who embrace data science weaving into the organization and culture create systems of insight and outperform their competitors.
You should too!
