A multi-stage anticipated surprise model with dynamic expectation for economic decision-making

Area of Science:

• Behavioral Economics
• Cognitive Science
• Neuroscience

Background:

• Existing models fail to capture the multi-stage nature of real-life decisions.
• Human problem-solving often involves sequential processing rather than holistic evaluation.

Purpose of the Study:

• To propose a novel descriptive decision-making model for multi-stage problems.
• To incorporate perceived post-decision information and dynamic reference points.
• To explain economic paradoxes and gambling behaviors through a new theoretical framework.

Main Methods:

• Development of a decision-making model based on 'anticipated surprise'.
• Dynamic adjustment of reference points during mental simulation of event sequences.
• Application of the model to analyze multi-stage decision-making scenarios.

Main Results:

• The proposed model offers a framework to understand behaviors violating classical economic theories.
• It provides explanations for prominent economic paradoxes and gambling tendencies.
• The model integrates concepts from neuroscience (prediction error, replay) and psychology (affective forecasting).

Conclusions:

• The 'anticipated surprise' model offers a more realistic approach to multi-stage decision-making.
• The model bridges behavioral economics with neuroscience and psychological theories.
• Future experiments can investigate the neural and psychological underpinnings of this decision-making process.