This question can be understood and modeled from multiple physical, mathematical, and philosophical dimensions. Here are several typical paradigms of 'difficulty' that attempt to comprehensively analyze this issue:



1. Incompleteness of information and difficulties in prediction (mathematical perspective)



  • Many human behaviors are based on making decisions with incomplete information.


  • In the Bayesian framework, such decisions require the ability to estimate prior distributions and likelihood functions, but in reality, there are often 'black swan' events (proposed by Taleb).


  • Examples: Financial markets, life planning, medical diagnosis.



Challenge: The ability to predict future states is limited by noise, nonlinearity, and the curse of dimensionality.



2. Choices in high-entropy systems (thermodynamics and information theory)



  • The essence of choice difficulty is a 'high-entropy' problem. When faced with a vast state space, the difficulty of finding an optimal strategy increases exponentially (NP-Hard problem).


  • Humans often seek certainty (low-entropy structures) from chaos, while the real world tends to increase entropy.



Challenge: Determining goals and paths in high-degree-of-freedom systems (such as career choices, marriage, formation of ideals).



3. The contradiction of self-reflection and consciousness (physical and philosophical aspects)



  • Humans can be aware of their own mortality, and this reflexivity brings existential anxiety.


  • Heidegger referred to this as the state of existence 'being-towards-death,' which is essentially an inherent tension of reflexive structures.



Challenge: The reflexive structure of consciousness leads to an inability to completely 'objectify' oneself, meaning one cannot fully see their own system from an external viewpoint.



4. Conflict between collective rationality and individual rationality (systemic game theory)



  • Models like the tragedy of the commons and Nash equilibrium reveal: individual optimality is often not collective optimality.


  • Humans must weigh trade-offs in social systems, facing moral paradoxes, issues of justice in distribution (such as climate issues, AI ethics).



Challenge: Maintaining stability in system games rather than heading towards collapse.



5. The incompleteness of quantifying feelings and meaning (mathematical logic and undecidability)



  • Gödel's incompleteness theorem reveals: in any sufficiently complex system, there are propositions that cannot be proven true or false.


  • Similarly, human emotions (love, pain, loneliness) are difficult to be perfectly expressed and compressed by formal systems.



Challenge: Subjective meaning cannot be completely formalized, making it difficult for machines to fully 'simulate'.



In summary, the most difficult challenge for humans may not be a single task, but:



In an incomplete, entropy-increasing, reflexive, and mutually coupled world, continuously constructing meaningful, directed, and coordinated life structures.



I propose an extensible question:


Do you think humans can use mathematical models to characterize the concept of 'meaning'? Or, is meaning essentially a structure that compresses information and delays entropy increase?