Takuya!

The shift toward experimental development under uncertainty requires embracing iterative learning rather than seeking definitive answers prematurely.

So "asymmetric advantage" can be generated by collectively investing in systems that are built to scale, as well as supporting rapid one-off testing of ideas: small, low-cost, and expendable software and models.

Asymmetric advantage, to me, means being able to exert large impact relative to the amount of effort invested. For most, if not all, of us in the environmental sciences, there is no one tool or model to rule them all.

Australia faces compounding strategic risks with a shrinking warning window. In response, a core emphasis is to invest in experimental approaches to accelerating asymmetric advantage through innovation partnerships.

Today's plenary talk at #modsim2025 was from Nigel McGinty from Defense Science and Technology Group (DSTG). Treating it like Sun Tzu's "The Art of War", here are my takeaways with an environmental decision support lens.

"Robust models don't grow on equations (or data) alone"

Software engineering of the model is essential

Overall an excellent example of research software development and science in action!

APSIM developers rely on automated test suites to ensure changes still meet expected behaviour.

In addition to software tests that apply observed data, we should also incorporate scientific understanding and knowledge. This is similar to property-based testing.

But what if you care about model outputs at lower-than-system levels? Does the model meet expected rules of thumbs?

Individual components are not calibrated. Instead, calibration is done with the final model after components are coupled, with their emergent behaviour assessed. The model is tested at the system level.

Today's plenary at #modsim2025 is by Val Snow, Bioeconomy Science Institute

She presents APSIM, an example of a modular component and processed-based modelling framework, where individual models are coupled together to represent a system (in this case, agricultural).

Modelling indicates use of tech-enhanced care could have a wide range of benefits including reduced wait-times due to less pressure on the healthcare system

Participatory model development approach helped improve and correct the model to better represent young people's lives experiences.

Hossein Hosseini and team at #modsim2025 models the use of tech-enhanced care could improve mental health outcomes for people aged 15 - 24 with a System Dynamics approach.

"Technology-enhanced care": facilitating integrated and multidisciplinary care

Past iEMSs Presidents Tony Jakeman and Stefan Reis (left and centre right) with current iEMSs President Val Snow and my lowly self!

Great start to #modsim2025 !

Here's to a week of learning and reflection!

Trust is the currency of science. Without trust, we will fail to make a difference. Our models must be transparent and robust. We must be honest about uncertainty and limitations.

On trust and distrust in science:

Journal papers alone are insufficient to make impact.

Modelling is not just about algorithms but about people, biases, ..., and how it shapes our decisions. Australia trust scientists compared to other countries, but we are low in impact.

[cont.] Modelling is the bridge between discovery and decision. From the laboratory to the real world to help us make fair and just decisions.

#modsim2025

Paraphrasing Craig Simmons, Chief Scientist of South Australia:

We must remind ourselves that modelling is not only a science but very much an art. To make some sense of complexity into not just real world insight but also action (1/2)

#modsim2025

Rust for data science? How is it?