Ben Matthews

  • New here on lemmy, will add more info later …
  • Also on mdon: @[email protected]
  • Try my interactive climate / futures model: SWIM
  • 0 Posts
  • 9 Comments
Joined 1 year ago
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Cake day: September 15th, 2023

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  • I use vscode as I develop this model in Scala3, whose language-server ‘metals’ integrates well with vscode, and when scala3 was new in mid-21 this was the platform they first targeted. But the scala command-line tools do the clever analysis, vscode provides the layout, colours, git integration, search/regex, web-preview etc… Now considering other options (eg zed) as vscode too dependent on potentially unsafe extensions (of which too much choice), also don’t want M$ scraping my code. Long ago when same model was in java I used netbeans, then eclipse. Would prefer a pure-scala toolset.


  • Hi, excuse me for replying so late, but i’ve been away from lemmy for.a while. Well, to summarise, the model calculates the future trajectories, of population, economy, emissions, atmospheric gases, and climate response etc., according to a set of (hundreds of) diverse options and uncertainties which you can adjust - the key feature is that the change shows rapidly enough to let you follow cause -> effect, to understand how the system responds in a quasi-mechanical way.
    Indeed you are right, complexity is beautiful, but hard. A challenge with such tools is to adjust gradually from simple to complex. Although SWIM has four complexity levels, they are no longer systematically implemented - also what seems simple or complex varies depending where each person is coming from, so i think to adapt the complexity filter into a topic-focus filter. Much todo …


  • I can relate to this, having developed a coupled socio-emissions-carbon-climate model, which evolved for 20 years in java, until recently converted to scala3. You can have a look here. The problem is that “coupling” in such models of complex systems is a ‘good’ thing, as there are feedbacks - for example atmospheric co2 drives climate warming but the latter also changes the carbon cycle, demography drives economic growth but the latter influences fertility and migration, etc… (some feedbacks are solved by extrapolating from the previous timestep - the delay is anyway realistic). There are also policy feedbacks - between top-down climate-stabilisation goals, and bottom up trends and national policies, the choice affects the logical calculation order. All this has to work fast within the browser (now scala.js - originally java applet), responding interactively to parameter adjustments, only recalculating curves which changed - getting all these interactions right is hard.
    If restarting in scala3 I’d structure it differently, but having a lot of legacy science code known to work, it’s hard to pull it apart. Wish I’d known such principles at the beginning, but as it grew gradually, one doesn’t anticipate such complexity.