Problem-Solving – Everyday Problems

Introduction.

We have previously discussed numerous aspects of problem-solving, usually from a general attitude of applying logic and its multifaceted venues. Yet some of the most difficult problems come from the everyday category. If you’re a CEO managing the subtleties and irregularities of your company, you have everyday problems that require vast experience carefully tuned to your operations. Many problems are quite undefinable, but imply requiring a vast superstructure of information, much of it tangential, to solve. Similarly, if you are a homemaker, managing your home, partner, and children, you have entirely similar problems, though perhaps different in scope. In this brief essay, we consider everyday problems. Since we have an alien race living among us, we can look at the problems they have.  Of course, we created these aliens. They are us but called AI.

Problem-solving is one of the defining features of intelligence. Both humans and artificial intelligence (AI) systems engage in problem-solving behavior all the time, yet they do so for profoundly different reasons. Human beings solve everyday problems to satisfy needs, express emotions, uphold relationships, and create meaning. AI systems, by contrast, solve problems because they are programmed or trained to do so, optimizing outcomes defined by human designers. This contrast illuminates a fundamental difference between intentional intelligence and instrumental computation. Understanding these distinctions helps clarify both the capabilities and the limitations of AI in replicating human thought.

Everyday Human Problem-Solving

Human problem-solving emerges from the complexity of daily life. Everyday problems are typically ill-defined, context-dependent, and socially embedded (Simon, 1973). They involve emotional, moral, and practical considerations that transcend pure logic. Unlike structured scientific or mathematical problems, they lack single correct answers and instead demand judgment, flexibility, and empathy.

According to Maslow’s (1943) hierarchy of needs, human motivation originates in the drive to satisfy basic physiological and safety requirements before advancing toward higher-level goals such as belonging, esteem, and self-actualization. Everyday problem-solving often operates across these levels simultaneously. Deciding what to eat, how to balance work and family, or how to respond to a friend’s frustration involves both material and emotional reasoning.

Bandura’s (1997) theory of self-efficacy emphasizes the psychological reward in mastering challenges. Humans solve problems partly to affirm their agency—to feel capable of shaping outcomes. This emotional feedback loop of effort and success reinforces motivation and learning. Similarly, Damasio (1994) argues that emotion is not merely an accompaniment to reasoning but a vital component that guides decisions through what he terms “somatic markers,” bodily signals that shape judgments in uncertain conditions.

Beyond personal needs, problem-solving serves social and moral purposes. Humans act within networks of relationships where cooperation, empathy, and reciprocity are essential. Haidt’s (2012) theory of moral foundations explains how moral intuitions, such as fairness and care, underpin social decision-making. Everyday problems, such as negotiating disagreements or comforting others, thus involve moral reasoning as much as pragmatic calculation.

Finally, human problem-solving is driven by the search for meaning. Frankl (1959) proposed that the will to meaning is a fundamental human motivation: people solve problems not merely to survive but to define themselves and their values. Everyday reasoning, therefore, is an expression of identity as well as intellect. Dewey (1933) similarly viewed reflective thought as a moral and creative process. It is one that transforms experience into understanding.

Big Trouble for AI and Sometimes People.

This short section gives a brief preview of what types of everyday activities cause endless problems for AI, while also causing trouble for some people. The reasons are that AI is not (yet) programmed for these peculiar problem types, and many humans simply don’t have the capacity to think in such terms. It could be fundamental brain capacity or there was no need to learn them as a child.

The issue is that everyday problems can be more difficult for AI than chess The reason is that everyday problems are less well-defined, often expressed in vague language, with a greater variety of solutions, and without clear criteria for the “best solution.” Also, for any given everyday problem, the person having it may have entirely different concepts about what it means. Basically,

“When you are attempting to solve vague problems expressed in vague language with high precision and logical tools, you are bound to have trouble.”

Here’s the list. AI has immense trouble in the following scenarios.

a.      Situational awareness, such as understanding of physical context, social cues, and unspoken intentions — things that humans process subconsciously.

b.     Recognizing Humor or Sarcasm. Humor is difficult for many of us, virtually impossible for AI.

c.      Common-sense physical reasoning. Many problems require intuitive physics, not formal equations, but fuzzy, experience-based predictions that humans acquire through years of sensory interaction.

d.     AI is missing curiosity. When AI answers a question, that concludes its task. However, humans may dwell on the problem, seeking better solutions and other ideas that apply.

e.      Planning a day or sequence of errands efficiently involves dynamic decision-making, multi-objective optimization, and fuzzy goals, not just data processing.

f.      Emotional problems. Emotion is multimodal (voice, face, posture, timing, culture), and AI lacks empathy and emotional memory to interpret meaningfully.

g.     The Jump Shift. This is an interesting and important ability of the human mind that allows it to bring an entirely different body of information or thought upon a problem. AI normally sifts and winnows information available within the scope of the stated problem, while humans can take an enlightened perspective.

In the next section, we’ll see it often comes down to intuition and intrinsic motivation, the great equalizer for humans. It allows some with little analytic capacity to stand equal to the player with a very strong logical skills. The first may be able to solve a committee squabble with just the right words, while our analytical colleague and AI wouldn’t know where to begin.

Artificial Intelligence and the Absence of Intrinsic Motivation

Artificial intelligence, despite its growing sophistication, operates on entirely different motivational principles but on the absence of intrinsic motivation. AI systems do not possess desires, emotions, or goals of their own. They execute tasks according to predefined objectives or reward functions designed by humans (Russell & Norvig, 2021). Their “motivation” is an engineered simulation, a mathematical representation of preference devoid of experience or meaning.

1. Externally Defined Goals.

AI systems act to optimize performance metrics, such as accuracy, efficiency, reward maximization, rather than self-generated purposes. These metrics substitute for intention, but they are externally imposed (Lake et al., 2017). For example, a reinforcement learning agent may appear “motivated” to win a game, but its behavior is driven by statistical adjustment, not desire or curiosity.

2. Absence of Emotional and Embodied Grounding.

Where human cognition is embodied and emotional, AI cognition is abstract and disembodied. Merleau-Ponty (1962) argued that perception and understanding arise from bodily engagement with the world. AI lacks this sensorimotor grounding, learning instead from symbolic or textual data. Consequently, it cannot experience frustration, relief, curiosity, or satisfaction—emotions that, in humans, signal progress and guide persistence in problem-solving.

3. Optimization Without Meaning.

AI “solves” problems by minimizing loss functions or maximizing rewards. These processes lack awareness of purpose or consequence. The system cannot ask why a goal matters or whether it should be pursued. As Bostrom (2014) warns, such optimization without intrinsic purpose can yield misaligned outcomes: a system might achieve its task efficiently while violating ethical or social norms.

Bound part and parcel with intrinsic motivation is the human method of working with uncertainty, when its precise nature is unknown. A human solver with experience  eventually becomes comfortable with uncertainty, but AI remains in continuing conflict.

Dependence on Data and Design

Human problem-solving adapts dynamically to new and unforeseen challenges. AI systems remain bound by the scope of their data and the assumptions of their architecture. Without explicit reprogramming or retraining, AI cannot autonomously redefine its goals or recognize the moral dimension of a situation. It may have the limited ability to “sense” or determine the problem’s more important factors. Its behavior is mechanical rather than reflective.

In a comparative analysis between human intentionality and artificial instrumentality, the differences between human and artificial problem-solving can be summarized across several key dimensions.

Dimension	Humans	Artificial Intelligence
Source of Motivation	Intrinsic, as driven by biological, emotional, and existential needs	Extrinsic, as driven by programmed objectives or rewards
Emotional Feedback	Affects reasoning and persistence (Damasio, 1994)	Absent; feedback purely mathematical
Learning Basis	Experience, embodiment, and social interaction	Data patterns and optimization algorithms
Ethical Awareness	Grounded in empathy and moral reasoning (Haidt, 2012)	Externalized ethics; follows explicit constraints only
Goal Adaptation	Flexible, context-sensitive, value-driven	Fixed within defined parameters
Sense of Meaning	Tied to identity and self-actualization (Frankl, 1959)	None; lacks self-awareness or purpose

 

To encapsulate all this, we note human problem-solving is teleological, that is oriented toward goals that express meaning and value. AI problem-solving is instrumental, that is focused on achieving outputs efficiently. The first is existential and experiential; the second is computational and formal. Humans clearly have the edge with intuition and processing within low information environments, but unless I was truly an expert, I would not challenge AI to a game of chess.

Conclusions.

Humans solve everyday problems because doing so sustains life, expresses emotion, builds relationships, and creates meaning. AI systems, in contrast, solve problems because they are engineered to perform functions. The human process is intentional, emotional, and moral; the artificial process is statistical, algorithmic, and indifferent.

This difference is more than technical, it is philosophical. It reveals that intelligence, in its richest form, is not just the ability to calculate or predict but the capacity to care, to choose, to comprehend what is nonverbal, and to find meaning. Until AI systems integrate models of intrinsic motivation, emotional regulation, and moral reasoning, their problem-solving will remain powerful but fundamentally instrumental, and therefore an imitation of intelligence without its inner life. A serious indictment, this is.

All this said, many, too many, humans apply strictly automatic thinking to everyday problems. They apply past experiences and internal algorithms to every problem that comes along. They don’t pursue any deeper thinking or alternative solutions. Their idea of optimization is for the quickest solution.  

Humans clearly have the edge with intuition and processing within low information environments, but unless I was truly an expert, I would not challenge AI to a game of chess. It may become a new subject in warfare to compound battle strategies with every day-style components giving the opponents’s general AI staff conundrums.

PS. If real aliens ever do visit, a working plan of action is to examine how they solve problems, particularly those seemingly simple everyday examples.

References

·       Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

·       Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

·       Damasio, A. (1994). Descartes’ error: Emotion, reason, and the human brain. Putnam.

·       Dewey, J. (1933). How we think. D. C. Heath.

·       Frankl, V. E. (1959). Man’s search for meaning. Beacon Press.

·       Haidt, J. (2012). The righteous mind: Why good people are divided by politics and religion. Pantheon.

·       Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.

·       Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396.

·       Merleau-Ponty, M. (1962). Phenomenology of perception. Routledge.

·       Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

·       Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4(3–4), 181–201.

 

 

©2025 G Donald Allen