| Abstract |
In a representative democracy, like in The Netherlands, every voter expresses at a fixed collective moment his/her preferred delegate (i.e. a political party) that
acts as a proxy on defending him/her opinions and decide on their behalf for the national matters for four years.
The advantage of a representative democracy compared to a direct democracy (where every voter can directly vote for solutions), is that most voters not have the time, incentive and/or expertise to be involved in this (complex)
daily political decision making process (which is often a criticism on the validity of referendums) and delegate it to their preferred group of experts (politicians).
The selected politicians have to propose solutions for existing, but also new problems that occur during this long period. The solutions should closely reflect the basic principles which they promised to defend during the election period. This complex task has the permanent risk of making decisions that might invoke disapproval of the supporting voters, resulting in a decline of trust in the politician, party (endangering the chance of being re-elected), and the trust in the implementation of our democracy.
How can we, as AI researchers, help the politicians with this complex task and increase the probability that they reflect the opinions of their supportive voters during the long governmental period which as a consequence also would improve the trust by the voters in our democratic system? |