Probably nothing new, but I just wanted to note that when you couple two straightforward Google tools, Maps and a large enough fleet of self-driving cars, they are likely to unintentionally agentize by shaping the traffic.
For example, the goal of each is to optimize the fuel economy/driving time, so the routes Google cars would take depend on the expected traffic volume, as predicted by Maps access, among other things. Similarly, Maps would know where these cars are or will be at a given time, and would adjust its output accordingly (possibly as a user option). An optimization strategy might easy arise that gives Google cars preference over other cars, in order to minimize, say, the overall emission levels. This can be easily seen as unfriendly by a regular Map user, but friendly by the municipality.
Similar scenarios would pop up in many cases where, in the EE speak, a tool gains an intentional or a parasitic feedback, whether positive or negative. As anyone who dealt with music amps knows, this feedback appears spontaneously and is often very difficult to track down. In a sense, a tool as simple as an amp can agentize and drown the positive signal. As the tool complexity grows, so do the odds of parasitic feedback. Coupling multiple “safe” tools together increases such odds exponentially.
Google maps finds routes for individual users that rank high in the preference ordering specified by minimizing distance, expected time given traffic, or some other simple metric. The process for finding the route for any particular individual is isolated from the process for finding the route for other users; the tool does not consider the effect of giving a route to user A on the driving time of user B. Such a system is possible to design and implement, but merely giving Google maps data of where a particular class of users are driving in real time, and having those users request routes in real time, does not change what algorithm Google maps will use to suggest routes, even if another algorithm would help it better optimize driving time, the purpose for which its current algorithm was programmed. Google maps is not meta enough to explore alternate optimization strategies.
(And if the sufficiently meta human engineers at Google were to implement such a system, in which other users were systematically instructed to make sacrifices for the benifet of Google cars, the other users would switch to other mapping and routing providers.)
in which other users were systematically instructed to make sacrifices for the benifet of Google cars
I agree, but this is only one possible scenario. It is also likely that a fleet of Google cars would benefit the overall traffic patterns by routing them away from congested areas. In such a way, even giving priority to Google cars might provide an overall benefit to regular drivers, due to reduced congestion.
In any case, my point was less about the current implementation of Google Maps and more about the possibility that combining tools can lead to parasitic agentization.
Probably nothing new, but I just wanted to note that when you couple two straightforward Google tools, Maps and a large enough fleet of self-driving cars, they are likely to unintentionally agentize by shaping the traffic.
For example, the goal of each is to optimize the fuel economy/driving time, so the routes Google cars would take depend on the expected traffic volume, as predicted by Maps access, among other things. Similarly, Maps would know where these cars are or will be at a given time, and would adjust its output accordingly (possibly as a user option). An optimization strategy might easy arise that gives Google cars preference over other cars, in order to minimize, say, the overall emission levels. This can be easily seen as unfriendly by a regular Map user, but friendly by the municipality.
Similar scenarios would pop up in many cases where, in the EE speak, a tool gains an intentional or a parasitic feedback, whether positive or negative. As anyone who dealt with music amps knows, this feedback appears spontaneously and is often very difficult to track down. In a sense, a tool as simple as an amp can agentize and drown the positive signal. As the tool complexity grows, so do the odds of parasitic feedback. Coupling multiple “safe” tools together increases such odds exponentially.
Google maps finds routes for individual users that rank high in the preference ordering specified by minimizing distance, expected time given traffic, or some other simple metric. The process for finding the route for any particular individual is isolated from the process for finding the route for other users; the tool does not consider the effect of giving a route to user A on the driving time of user B. Such a system is possible to design and implement, but merely giving Google maps data of where a particular class of users are driving in real time, and having those users request routes in real time, does not change what algorithm Google maps will use to suggest routes, even if another algorithm would help it better optimize driving time, the purpose for which its current algorithm was programmed. Google maps is not meta enough to explore alternate optimization strategies.
(And if the sufficiently meta human engineers at Google were to implement such a system, in which other users were systematically instructed to make sacrifices for the benifet of Google cars, the other users would switch to other mapping and routing providers.)
I agree, but this is only one possible scenario. It is also likely that a fleet of Google cars would benefit the overall traffic patterns by routing them away from congested areas. In such a way, even giving priority to Google cars might provide an overall benefit to regular drivers, due to reduced congestion.
In any case, my point was less about the current implementation of Google Maps and more about the possibility that combining tools can lead to parasitic agentization.