Yet again I bid you a happy Friday, fellow pilots!
It's been a fun two weeks. I've concentrated my efforts entirely on the game simulation and high-level AI, in pursuit of a working economy, as per my last log. I'm pleased to report that, after two weeks, we do indeed have a small but working economy happening.
Major Additions Since Last Time:
- System 'economy caching' ported & re-worked from old code; helps AI agents reason about job and market availability within a system or zone
- High-level AI reasoning; forms the basis of AI players' ability to dynamically choose a profession based on profitability analysis
- Basic colony population dynamics (helps create a time-varying economic sink/demand for basic goods, thus seeding the system economy)
- Market mechanics now fully-implemented including escrow, on-station/on-colony storage lockers for temporary storage of bought goods or canceled sell orders, etc etc.
- Limited implementation of Zones -- already in-use by AI for reasoning about job locations, but no zone gameplay mechanics (ownership, laws, etc.) yet
- Happened upon a new algorithm for individual asteroid/ice/debris/whatever placement within fields, resulting in much more natural looking fields (no longer are they obviously ellipsoids )
At this point I've ported most if not all of the important simulation & high-level AI features that were previously implemented, meaning that I'm now getting to think about and solve new problems -- a welcome departure from porting! The next step for me is smoothing out the volatility of the economy and AI behavior. It's somewhat interesting that periodic/cyclic patterns always seem to emerge in my basic simulated economies when AI agents don't have access to historical data. That result is pretty obvious I guess, but still, interesting to see "those who don't remember the past are doomed to repeat it" play out so literally on-screen. The cyclic behavior can be seen as far back as Development Update #15 (March 2014!), when I introduced colony dynamics and AI job switching for the first time. I've never done a great job of smoothing over this volatility before, but I'm quite convinced that it's a pretty simple matter of factoring in historical data (EMAs mostly) + having a distribution of various AI behaviors with respect to time scale. Some AI agents should act on fast-moving EMAs, making 'short-sighted' decisions about jobs & markets, while others should act on slow/long-period averages, making 'long-term' decisions -- together, the result is a smoothing of the economy at all time scales.
Here you can see the overly-volatile economy in an 8-planet (the other six are off-screen), 50K AI agent simulation. Notice the jagged population graph as well as the obviously-visible 'flocks' of blue AI ships, which are due to market conditions changing so rapidly that thousands of AI units decide to change jobs all at once, hence the 'mass migrations.' Of course, so many units changing behavior all at once will cause yet another major shift in market conditions later on, which will, in turn, produce yet another flock of dissenters, and so on, ad infinitum With historical averages factored in, this would be a different story.
(NOTE: I know these screenshots are atrocious, but that's part of the point. When I work on the game simulation, I need to be focused 100% on behavior & dynamics, and 0% on graphics/tertiary concerns! As you can see, that is very much the case here )
Here you can see how a colony that has just recovered from a population crash (and is about to experience a large period of growth) is attracting droves of water traders due to high demand and correspondingly-high prices. Having no access to historical data, the traders are doomed to oversupply the colony, indirectly setting the economy up for the next crash.
I've spent a fair amount of time this week reading papers on market economy simulation (of our own planet, just to be clear). Never before have I really dove deeply into the colony simulation; previous iterations of colony dynamics were still quite placeholder, and really just designed to create an elastic demand for basic goods. The problem of colony simulation is important to me not only because I want the simulation dynamics of LT to create interesting, meaningful behaviors and opportunities, but also because the problem of simulating a colony is precisely the problem of performing a coarse simulation of a (sizable) economy (which is important to us for many reasons, including OOS system simulation and historical simulation at universe-creation-time). Ideally, insights uncovered in my quest to implement a decent colony simulation will bear fruit that can be applied toward the 'big daddy' of remaining problems in LT development: OOS/historical simulations.
Thus far, research has been fairly uninspiring. Many papers in this field address the elephant-in-the-room fact that the field itself has produced models of consistently-poor accuracy. It is not really surprising to me when you look at the models and equations in question Lucky for me, I don't care about predicting what will happen to the global economy of Earth...I only care about creating interesting dynamics for fictitious universes! Since I've been having trouble finding inspiring reading on this topic, I would welcome any sources that you guys might know of -- papers, articles, books or the like that you may have stumbled across that have good insights into quantitative models/simulations of global economies/populations/anything interesting. In the end I'm sure my model will end up being simple (like everything I love)...likely just a vector of quantities and a Jacobian of their relationships; but I do like being inspired along the way, and my brain is enjoying getting to read new solutions to new problems again!
Going forward, my next steps are:
1. Recording & factoring historical data into AI reasoning
2. Capital Expenditure in AI & simulation (purchasing new ships, building a new station, warp rails, etc.)
3. Information mechanics in AI & simulation
2 and 3 are both highly-unexplored territory for me, so I'm excited to dive in. Information, in particular, is one of the few remaining 1.0 mechanics that really lacks in past or present implementation. I did have information itself implemented in LTC++, but none of the AI algorithms actually used information correctly. The ability of an AI agent to perform a job should depend on whether or not the agent actually knows about the location and/or associated object of the job. In addition, AI agents need to be able to place value on information that 'unlocks' new job/action possibilities, which strikes me as being very similar to capital expenditure in the sense that it's a one-time cost that provides continuous future benefit (it is inherently difficult to formulate a 'correct' value for such costs).
I'm hoping to have all of this (economy, simulation, high-level AI) in good shape by the end of the month or perhaps in another two weeks. That's an ambitious goal, to say the least -- we're talking about a pretty massive chunk of what makes LT LT here. Still, I think it's at least possible to have the framework and general strategies for all of this done by then. Naturally I will have to tweak constants and so forth when we playtest and realize that the AI is actually too smart and is ruining the player experience ( ), but having all of the algorithmic bits and general solutions in place will certainly make me feel better about remaining dev effort.
That's all for today, back to coding, see you soon o7