Response Coordination

TIME TO STRATEGY EXECUTION: 68 DAYS

Maura officially remains a special agent of the U.S. Extinction Response Unit. Prior to being attached and operationally reporting directly to WICO she worked out of the USERU’s field office in Denver, developing local strategy options and helping identify their potential outcomes (the latter of which she was doing with the global strategy when I was summoned). Today we visited the field office, whose personnel have been tasked with coordinating the strategy’s execution in the Rocky Mountain states.

Regional Director Felicity Jonas greeted us warmly and then compared notes with Maura on the status of preparations for the roll-out. After the attack on WICO, USERU made educated guesses about that the final strategy would look like, with emphasis on the national strategy’s inputs, and yesterday finished a review comparing their guesses with the current version of the strategy. Sally had been particularly helpful in the review, which she was simultaneously doing with extinction response units in the other nations. 

“Our guesses were pretty close,” Jonas said as the briefing wrapped up. “Locally we have a couple dozen action items that we can address by the end of the month, no sweat. After that, and until the execution date, we’ll be enlisting public and local governments to refine the impact reduction criteria and translate them into activity plans on a granular level. Do you think you can help us with that, Maura?”

“That’s one of the reasons I’m here,” she replied, giving me a knowing look. “I’m expecting a full report on my team’s personal environmental assessment suggestions and related test plans by end-of-business today. I’ll review them tonight, and I’d like to get your take on them tomorrow morning.” She explained that individuals could use such approaches for high-level detection and assessment during the initial phase, while more technology intensive approaches would be applied to conditions expected to be too large or unsafe. “It will improve the overall efficiency, and give us critical feedback for developing the next version, which will eventually be dominant. For those reasons, we should test them as soon as possible. After you see what we’ve got, I’d like to brainstorm how we can leverage what you’re doing with the activity plans.”

“Agreed,” Jonas said. “Meanwhile, I’ll pass this up the chain of command to see if any other regions can get involved.”

“Will’s next blog post should make that easier,” Maura suggested.

After we left, she suggested we do some sight-seeing and talk about the next steps, beginning with the radical idea I mentioned in yesterday’s post.

As she drove us into the mountains, I gave her an overview. “It’s related to a discussion I had with Sally back on February 7. I know because last night I looked up a post that I wrote then. Ambassador Lazlo even commented on it the next day. People are reacting to their environmental conditions in some ways like other animals do. We’re so used to looking at big picture statistics that we don’t see how it can scale to everyday experience.”

I waited for a reaction. “You mean, people are the detectors?” she asked.

“You got it,” I confirmed. “When people lived in nature all the time they were doing exactly what we want to do, with nothing more than what they could carry. We evolved to routinely make environmental assessments just to survive. Clearly some of it is still happening, affecting how happy we are, how many children we have, and how long we each live. If you believe Sally’s statistics, it even affects how much we trade with each other.”

“But life expectancy is tied to technology,” she argued, “and the economy depends on who is trading with who.”

I had thought a lot about those questions before falling asleep. “We still get sick, even fatally so, which is a direct effect of the toxins we breathe, drink, and eat. As for the economy, the quality and distribution of resources are averaged out in the stats, but they don’t have to be.”

“You sound more like a scientist than a journalist every day,” she observed.

“I like to read as much as I like to write. Also, I have a lot of smart friends.” She smiled, but I was specifically thinking of someone else. “You know what? I think it’s time for you to meet one of those other friends.”

Reality Check

Near the end, Will was of course referring to the correlations between remaining ecological resources and the global variables he cited. In my simulations, I have not used specific distributions of resources but rather inferred them from historical trends, essentially treating populations as resource distribution detectors. In the characters’ quest to test, I’m basically presenting a case for testing the assumptions and results of the simulations on small scales.

Errors

TIME TO STRATEGY EXECUTION: 109 DAYS

The logical first step in my contribution to troubleshooting the artificial intelligence tool Sanda was to learn about the strategy inconsistencies found by the test team. I spent most of yesterday morning being briefed by the head of the team, Caleb Tosner, who is about my age and a lot smarter. 

I can’t go into detail for security reasons, but there were generally three types of what the team’s test plans classify as “critical errors.” 

The most consequential error had to do with changing the way people make economic decisions. Tosner explained, “A typical region is the size of a very large city, so we have to start with what's already in place. Governments and businesses have historically tended to promote growth in revenue, but they will have to substitute that with growth in nature, and without any way to pay for it. The AI was supposed to use the national strategy inputs and models of law, psychology, and behavior to develop a set of agreements people and organizations could make both within regions and between regions to enable that.” He displayed a short, bulleted list on his conference room screen. “This is what Sanda gave us. It is essentially gibberish, and inconsistent with both the inputs and the models. Our testing includes implementation of relevant strategy components in small cities populated with volunteers, and then observing the results. No one knew what to do with this, or these.” He replaced the list with ten more, in rapid succession, each appearing to be a set of generalities, rather than specifics, regarding a different policy requirement.

“That’s the first type, agreements,” he told me. “The second type is projected conformance with target measurables like population, ecological impact, and life expectancy.” He displayed two graphs side-by-side, each with a time series of the three variables he mentioned. The graph on the left was clearly the goal; while the one on the right had population that was too high and per-capita impact that was a little low toward the end. “This was the easiest failure I’ve ever seen in a test,” he said, disgusted. “The AI explained that the reduction in population could not be justified, and that the difference in ecological impact was too small to be worth changing. Can you believe that? The AI even signed off on the targets at the beginning after doing most of the work deciding what they should be. My teenager has better excuses!” He added that the test team was re-running the numbers to make sure that the projections reflected the design and agreed-to assumptions in the strategy; and was planning to re-evaluate the assumptions, as time permitted, using recent research results and observations in the test cities, some of which were in the process of major environmental remediation. 

Finally, there was variability. Sanda was giving different answers to the same questions during the week before the server crash. This didn’t technically indicate a problem since the questions had nothing to do with the work and might leave room for interpretation, but some members of the test team flagged it as an anomaly worth investigating (a “potential bug” in their jargon) and it was getting a lot more attention in light of the other errors. “This looks like a good one for you to start with,” Tosner told me toward the end of the briefing, and I had to agree.

Reality Check

In lieu of having a real artificial intelligence tool to mimic, I’m imagining Sanda as a person with reasonable limitations and strengths represented by placeholders for what I don’t know or have.

I continue refining the simulation to better model what the imaginary world (and ours) might encounter, and how it might decide to handle issues suggested by the output.