Difference between revisions of "Open Politics"
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== Schedule ==
== Schedule ==
Latest revision as of 14:10, January 18, 2011
The concept "Open Politics" refers to a massive multi-player online game.
- democracy simulation, players act as (prospective) politicians
- backend and frontend open source
- frontend HTML+CSS+JS for modern browsers
- game-play should be somewhat realistic
- game should accommodate people logging in once or twice per week but still provide fun for people logging in once or twice a day
- emphasis on the player community
- some inspiration comes from Power of Politics but the concept has grown far beyond that.
- Players can create multiple characters, which are all shown on the player profile (need to clear up how to deal with espionage, ideally build it into game mechanics).
- Characters can be retired at any time, when reactivated, time-based decreases apply as if they had been active.
- A character starts off a place where (s)he lives and can join or found a party but doesn't have to.
- To build up some credibility, a CV with a limited amount of years.
- The character can gain (per-region) popularity by appearing in public and campaigning, but people forget about that over time.
- Money comes into the pockets by either working a job, go for fundraising (on the streets or at businesses), getting money from a party (as allowed by government decisions), being in an elected position, or doing jobs for a party.
Parties and Alliances
Terms and Elections
- Term length is decidable by the government, 2-8 weeks, default is 4.
- List or person elections by gov. decision
- Electoral system can be decided by gov. between a small number of choices/algorithms.
- Every region has a mix of different population groups (not constant, but changing just slowly in reaction to political decisions and random factors), who weigh different factors of politicians' and parties' personalities differently when deciding their votes. Campaign modes and election systems influence how person and party components factor in. The algorithm calculates a few stereotypes per region and applies statistical functions to distribute those results across the population.