Computational concept for human food choice and eating behaviour

An inadequate diet can cause a number of illnesses with some them posing major threats for humanity. Poor largely originates from behavioral and social issues rather than environmental factors. With simulation being grand tool to analyze address behavior issues, relatively few studies focus on computational modeling nutrition at behavioural level. Furthermore, we have overviewed several popular approaches simulating dietary decision-making found no clear favorite. Further still, modelers rarely pay attention one the key factors – motivation. In vast majority models, either motivation is assumed be exogenously given and, hence, left out model, or not taken into account in any form, even though ignoring incentives significantly reduces adaptive capabilities human-like goal-directed model entity. We aimed outline modelling approach that would fit food choice topic improve available models. This implies offering an intelligible algorithm easily applied statistical data yet depth analysis despite its seeming simplicity. Thus, present our view problem which employs eating original mechanism different both traditional maximizing common economics artificial intelligence research dominant psychological approaches. outlined programming conceptual involves sequential incentive (which result biological necessities, social, intellectual spiritual needs alike) selection, incentive-foodstuff coupling (a relation fixed dynamic) elimination undesirable options based ranking (qualitative seems preferable over quantitative ranking, forasmuch as it resembles way thinking regular person more closely) supplemented by pseudocode segments. The suits agent-based paradigm, tied fitted other well. supposed implemented Java. Since offered requires implementation bring about robust conclusions goal reach next.