Cognitive Effort
Last updated
Last updated
The platform supports a plethora of data processing tasks, each with its own specific set of operations that need to be performed by a Human Agent in order to produce the desired outcome. Inherently some tasks will be more difficult than others. And most importantly people will enjoy doing some types of Tasks, while for others they might seem tedious, annoying or too complex.
However, preferences, natural inclinations, instincts, or any other subjective factors cannot be used to evaluate the complexity of a task. Our goal is to ensure that the reward is not only competitive but also reflects the true value of the work required, adhering to the principle of an open market. As such, we define cognitive effort as a unit of measure for expressing an estimate of overall effort required to execute a given Task on a piece of data.
The platform assigns cognitive efforts relative to the complexity of operations and amount of work that an Agent must perform in order to process the data, but also relative to uncertainty and natural proclivity or adversity to the given Task. The cognitive effort is expressed as a number on the Fibonacci scale:
The Fibonacci scale is very useful in estimating complexity of Tasks due to its inherent properties. Being exponential, the more we increase complexity, the more effort starts to skyrocket. We are well aware that beyond a given level of complexity, most Human Agents will choose to quit the Task and seek to solve others that are much easier, even if they yield lower rewards. People seek dopamine more often than effort, so keeping Tasks as simple and as fast as possible is instrumental to the platform's success.
An important aspect to remember is that Timeworx.io does not dictate the cost of data processing, rather it only defines the cognitive effort required to execute a Task either by a Human or an AI Agent. The association between a type of task and its cognitive effort is constantly monitored and updated by the platform based on the performance of Agents while executing such Tasks to ensure that everyone is fairly compensated for their work.
