It’s what fits the facts.
Then why do the people who are experts on this kind of thing not see it?
What do I know that they don’t?
Their medical examiner is competent enough, it would seem. Richards and Kendall know more about bears than I ever will. And Detective Glenn is no fool. After the animal was tracked down, he was still on the case.
If this was the first act of a movie, I’d be pointing the finger at him. I’m not very good at reading people, but in all my interactions with him, his suspicions were always directed at me.
I can’t rule anything out. Except one thing: I’m not the kind of person that could talk to someone for an hour and have any idea one way or another if they’re guilty.
All of those people collectively know more than me. Yet, here I am, staring at the ceiling, convinced Juniper’s killer walked on two feet.
Why?
What do I know that they don’t?
It’s not any one thing. My expertise isn’t deep in any field. My papers, my research, my life have been about drawing connections from very different fields. My domain is how things are related.
I trace life cycles. I look at gene flows. I build computer models and search for real-world analogues.
I seek out systems and circuits. Whether it’s the nitrogen in our bodies that came from fertilizer plants or it’s our genes for coding specific proteins that evolved a billion years ago.
Systems can go laterally through space. Others move linearly through time.
I get up from the bed, pull some maps out of my backpack, and tack them to the wall with glue dots.
I’m not a detective. I’m not a forensic specialist.
I’m a biologist and a computer programmer. These are my areas of expertise.
I stick a red circle where Juniper was found. Next to it I place a green one to represent that she’d physically been there. I place another on the car repair shop and another on her motel.
These are places where we know Juniper had been alive. It’s part of her graph. I place another on where she was working on her postgrad at Florida State and another where she lived in North Carolina. The final dot I place on Austin, where she was in my class.
These are points in her life graph. In a computer I can create a version that shows this over time. But right now it’s simple enough to see.
This is Juniper’s story.
Her life started in a delivery room in Raleigh. It ended in a forest in Montana.
What brought her to that point?
Life is decided by thousands of external and internal forces.
Her death could have been a random event, initiated by someone catching a glimpse of her as they passed her walking on the highway.
It could be someone she’s known for years, all the way back to North Carolina.
Maybe some FBI profiler could look at her wounds and tell you if it was personal or not. I wouldn’t know. And since the experts think it was a bear, I don’t know what credibility I’d give them right now.
I place a black circle next to the two by her body. This is her killer. We know at one point he was in the same place as her.
I place another where Bart was killed. Our killer was in that area at some point as well.
To be precise, I don’t know that the killer was there. It could have been an accomplice. Graphs don’t always measure actual locations of organisms. Sometimes they just map their influence. For now, I’ll just use black circles for the killer’s graph of influence.
The killer’s graph . . .
I sit back and take it in. I only have two data points, but that’s a start.
In my field, a graph can be just as illuminating as an actual animal or its DNA. Sometimes more so, because it can tell you how it lived and not just the color of its pelt or the arrangement of its genes. Sometimes less so, because a graph can be misleading. Too many unrelated data points leave you looking at chaos.
Sorting through chaos is why I developed MAAT. She’s the software I use to sort through thousands of points of information and find patterns.
MAAT is based on how I think, but much more advanced.
I built it using source code from a research project designed to find genes that contribute to longevity. It’s AI that builds better algorithms with each iteration. Each time becoming more and more complex.
I couldn’t tell you how the current version of MAAT works, just that she does. Sometimes.
When the researchers who developed the core AI behind MAAT asked it to figure out what gave a strain of fruit flies its longevity, it pointed to the genes that regulated resveratrol—the same chemical in red wine that has been tied to human longevity. When they tried to figure out why the software singled that out, the answer was a string of data that no human could understand.
What MAAT could tell you right now from the data points on my map is what is already obvious.
She’s really useful when you give her thousands or millions of points.
Points I don’t have. The killer is just two black dots in time and space. But . . . in the absence of firm data, the other trick is to give her assumptions.
If we were looking at mating cycles, and Juniper and her killer were two mountain lions, I could tell MAAT the frequency that a female is in estrus and an estimate of the male’s range. That information would give me an estimate about when they would encounter each other again. If a male mountain lion had multiple females it bred with and they had specific ranges, I might be able to predict where else he would show up.
And if there were general rules about the kinds of places they reproduced, I might be able to narrow down candidate spots based on available geographic information.
From all of this, MAAT could give me a dozen or so places where I could plant wildlife cameras and reasonably expect to catch the two large cats doing it, even over an area of dozens of miles—all of that based on three data points and general information not specific to an animal.
The problem is I don’t have any more data to put into MAAT.
I know nothing about the killer.
He was born at some point. He met Juniper. At some point after that, years or minutes, he killed her. His last appearance was getting her blood on Bart. Then he vanished from his graph.
I need more data than what’s on my map.
From where?
If I don’t have data, then I have to use the next best thing . . . which is also the worst best thing.
Assumptions.
I need to make guesses.
On a real graph these wouldn’t be black circles. They’d be half black, half white. They’re maybes.
Sometimes they lead you somewhere interesting. Other times they derail you for months . . . or years.
Our war on cancer has been filled with countless maybes. Billions of dollars and millions of human hours have been spent chasing after a pattern we can’t even begin to guess at.
Even still, we’ve made some progress. Many of those maybes have panned out. People live longer than before because not all that effort was wasted. And for every maybe that turns out to be a no, we still move forward.
I need some maybes and assumptions about the killer.
I can’t be worried if they’re wrong. I just need a starting point.
Let’s make some . . .
Juniper’s killer was clever because he got away with it. That’s a hard thing to do. He was either very lucky or experienced.
Okay . . . let’s go with experienced.
Oh, shit. Sometimes one assumption makes something else automatically true.
An experienced killer implies that he’s done this before . . .
I open up my laptop and do a search for bear attacks in the United States and Canada.
I’m not sure what I was expecting, but there’s only been a handful in the last ten years.
The Fish and Wildlife Service has detailed reports. Most of them are in deep woods. I look for any within a few miles of a highway.
There are two. In the first, three years ago, a self-proclaimed grizzly expert was killed. I’d personally rule that a suicide.