Sust(AI)nable Wisdom: Adapting to Our Logic Paradox
Artificial intelligence curates our choices and social media amplifies our fear of missing out, creating a logical dilemma: How do we make informed decisions when every seemingly attractive choice could potentially impact our outcome? This "fork in the road" of unlimited possibilities only emphasizes this paradox of choice.
Simple concepts may help connect the dots in our paradoxically wired lifestyles: examining how FOMO, the Monty Hall Paradox, and dialectical thinking might influence our decision-making in a digital environment where being informed is increasingly ambiguous.
The phenomenon of FOMO, the fear of missing out, is intensified by the frantic and extremely public nature of digital platforms. Internet users now spend an average of 2 and a half hours each day on social media, that's more than one-third of their total online time. This means that our deep-rooted concern about making the "wrong" decision or missing out on our traditional scenarios are even more complex with the ambiguous "better" alternatives that contradict each other.
This leads us to seek guidance from the principle of Dialectical Thinking, which suggests that by acknowledging our fears, questioning the intentions behind our choices, and weighing the overall implications of our decisions, we can operate risk-free, or at least with greater confidence and clarity, in the digital age. However, with digital "peer pressure" and the element of emotionality in our rationality, dialectical thinking may become time-sensitive.
Where logic has its limits, probability provides an alternative path.
The Monty Hall Paradox, a probability puzzle rooted in game theory, questions our intuitive understanding of choice and chance. It reveals how our initial choices, when faced with new information, may not always serve our best interests. This paradox not only intrigues, but also illustrates the dilemma of modern consumers and businesses: in an over-informed marketplace, is more choice necessarily better?
Given AI's ability to sort through massive amounts of data to simplify predictions and inform decisions, could it be configured to factor in the multifaceted human emotions, social context, and Monty Hall-like paradoxes?
My immediate response would be "of course!" But knowing that the sources for programming these complex generative models come from our own human experiences, how do we filter all of the data being input on a second-by-second basis to prevent the potential for getting us into even more "analysis paralysis" or leading to decisions that feel unfamiliar to our unique human perspectives? How can we prepare AI to augment, not override, the depth of our decision-making processes?
