Soma: /* Notes */

2025-01-16T21:06:03Z

Notes

← Older revision		Revision as of 21:06, 16 January 2025
Line 5:		Line 5:

	==Notes==		==Notes==

			(Sorry, no lecture recording for today.)

			<pre>
			Lecture 4
			---------

			responses overall good

			make sure to
			- make it clear you've done the readings
			(without summarizing)
			- point out what you didn't understand
			- say what you got out of them/what you didn't get out of them

			G1
			--
			- contrast between papers
			- Bateman, cooperation coming out of evolution
			- Michod, no thought but still cooperated
			- chicken & egg situation - evolution or trust?
			- wasp stripes copied by harmless insects
			- classifications aren't reliable when agents adapt
			- cheating - can it happen in computer systems? without intervention of people?

			G2
			--
			- Didn't quite understand free rider problem/cheating
			- how can you stop it?
			- torrenting as evolutionary
			- cooperating individuals sharing fragments
			- how fitness functions could manage cheating & encourage synergy

			G3
			--
			- Michod: how groups manage conflict/group fitness
			- groups managing conflict through cooperation
			- adaptation to help address conflicts
			- may favor group over individual
			- groups allow work to be split up, can add robustness
			- Bateson: inter vs intra-species cooperation
			- may not always be obvious,
			e.g. plants cooperating with animals, oxygen & carbon dioxide

			G4
			--
			- Michod had some confusing biology terms
			- altruism doesn't make sense in a computational context
			- don't want a system to have to be hacked before we can defend against it
			- hacks affect groups not individuals
			- in a group setting, can take advantage of individual strengths, account for weaknesses
			- but with computers, such "helping" each other has to be set up in advance
			externally
			- with computers, nobody wants to be the first victim

			G5
			--
			- both papers are about the challenge of cheaters in the context of cooperation
			- cooperation works because it is the optimal strategy, that's why it is sustained
			- that's why we want cooperation in computer systems
			- so build the system so cooperation is the only way it works,
			no way to win by cheating
			- e.g., blockchain
			- enforcement (e.g., patrolling cells)
			- with computers, who is doing the bad things? identification is much harder
			- economic perspective: cooperation gives a better payoff
			- cooperation good way to maintain efficiency in distributed computing systems
			- just show that it would be worse if you betray, no need to enforce,
			agents will behave better with right incentives


			Note these papers are about observations & theories
			- note the theories do not necessarily follow from the observations

			When you see a complex system, we can ask
			- how does it work?
			- how was it made? <--- evolutionary theories tend to focus on this

			from Michod
			- fragmentation is just computers/programs doing their own thing
			- aggregation is like software dev - combining a bunch of parts to make something that can be distributed
			- zygote/spore is like booting/orchestration - making a complex system
			from a much simpler one
			- big difference is trust, esp when considering microbiomes

			- we don't understand how living systems bootstrap themselves
			- how does the microbiome get going?
			- with computer systems, we often don't understand the booting process either!

			it isn't that computers are exactly like living systems
			- but in both we have to solve similar problems

			coordination & cooperation
			- trust in distributed computation
			- google systems vs oceanstore, untrusted systems
			- symbiosis, Margulis
			</pre>

Soma: Created page with "==Discussion Questions== * What did you not understand in the readings? Specifically, what biological terms/concepts would you like to learn more about? * How applicable are these readings to computational systems, in your opinion? ==Notes=="

2025-01-16T16:34:41Z

Created page with "==Discussion Questions== * What did you not understand in the readings? Specifically, what biological terms/concepts would you like to learn more about? * How applicable are these readings to computational systems, in your opinion? ==Notes=="

New page

==Discussion Questions==

* What did you not understand in the readings? Specifically, what biological terms/concepts would you like to learn more about?
* How applicable are these readings to computational systems, in your opinion?

==Notes==

EvoSec 2025W Lecture 4 - Revision history

Soma: /* Notes */

Soma: Created page with "==Discussion Questions== * What did you not understand in the readings? Specifically, what biological terms/concepts would you like to learn more about? * How applicable are these readings to computational systems, in your opinion? ==Notes=="