Key Terms, Examples, And Evidence From The Video: Cognitive ✓ Solved

Key Terms Examples/Evidence from the Video Cognitive Dissonance

The assignment requires an exploration of key terms like cognitive dissonance, motivated reasoning, selective exposure, particularized trust, information silo, filter bubble, echo chamber, and tribalism. You are to provide examples and evidence from the video discussed in the context of these terms.

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Cognitive dissonance is a psychological phenomenon where individuals experience discomfort from holding two contradictory beliefs or values at the same time. This discomfort often leads to changes in attitudes or behaviors to alleviate the inconsistency. In the context of decision making, cognitive dissonance can impact how leaders perceive and act upon data and models. For example, when presented with data that contradicts their beliefs about a project’s viability, decision-makers may initially reject the data in favor of their preconceived notions (Festinger, 1957).

Motivated reasoning, closely related to cognitive dissonance, refers to the tendency of people to draw conclusions that align with their desires and biases. In organizational contexts, this can manifest as selectively interpreting data to confirm existing positions, ignoring information that suggests the need for change. For instance, leaders may focus on positive indicators of a project's success while ignoring negative trends that may indicate risks (Kunda, 1990).

Selective exposure is another significant term in this discussion. It describes the practice of individuals favoring information sources or data that reinforce their existing beliefs. In decision-making, this can create echo chambers where diverse viewpoints are systematically excluded. Many organizations struggle with this, leading to a lack of diverse perspectives that could enhance decision-making processes (Stroud, 2011).

Particularized trust refers to a trust that is built within smaller groups or networks. This can create information silos where individuals only share and accept knowledge from trusted insiders, hindering broader organizational learning. In decision-making scenarios, this can lead to a narrow view of the data available, as the reliance on familiar information sources can overlook critical insights from external models or analyses (Putnam, 2000).

The concepts of information silo, filter bubble, and echo chamber are closely linked, as they represent environments where individuals are shielded from differing perspectives. Information silos can develop within departments where employees only engage with data that is relevant to their specific functions, missing interdepartmental insights. Filter bubbles occur primarily online, where algorithms curate content based on past behavior, reinforcing user biases. Echo chambers can form in any tight-knit environment where dissenting opinions are dismissed, further entrenching existing beliefs (Sunstein, 2001).

Tribalism, in this context, describes the tendency of groups to form around shared beliefs and identities, often leading to an 'us versus them' mentality. In decision-making contexts, tribalism can hinder collaboration and the integration of diverse models, resulting in poor strategic choices. For example, when teams are divided along tribal lines, they may resist adopting successful strategies utilized by competitor teams, believing their approach is superior (Pinker, 2011).

Acknowledging and addressing these psychological and social dynamics are crucial for organizations aiming for effective decision-making. By understanding cognitive dissonance and motivated reasoning, leaders can actively seek out diverse models and data sources to enrich their decision-making processes. This can involve encouraging open dialogue, fostering environments where differing opinions are valued, and creating systems that mitigate the effects of selective exposure and tribalism (Gigerenzer, 2007).

To combat information silos and echo chambers, organizations can implement practices such as cross-functional teams, workshops aimed at challenging assumptions, and utilizing technology to facilitate access to diverse information sources. The integration of machine learning and AI can also assist by analyzing patterns in data that human analysts might overlook (Page, 2018).

Ultimately, improving decision-making in organizations hinges on recognizing the limits of singular models and adopting a 'many-model' approach. The insights gained from multiple, diverse models can help mitigate cognitive biases and lead to more robust, well-rounded decisions (Munger, 1996). This aligns with Page’s argument that embracing complexity and diversity in thought processes yields better strategic outcomes. Therefore, organizations aiming to enhance their decision-making capabilities should cultivate an environment that champions diverse perspectives and constructive conflict (Page, 2018).

References

  • Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford University Press.
  • Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480-498.
  • Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. Simon & Schuster.
  • Stroud, N. J. (2011). Niche News: The Politics of News Choice. Oxford University Press.
  • Sunstein, C. R. (2001). Republic.com. Princeton University Press.
  • Pinker, S. (2011). The Better Angels of Our Nature: Why Violence Has Declined. Viking.
  • Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. Viking.
  • Page, S. E. (2018). The Model Thinker: What You Need to Know to Make Data Work for You. Basic Books.
  • Munger, C. (1996). The Uses of Models: A Relationship Between Theory and Practice. Journal of Economic Perspectives, 10(1), 37-52.
  • Page, S. E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press.

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