Would AI Perform Better If We Simulated Guilt?

Remember, it's all synthesized "anthropomorphizing". But with that caveat, Science News reports:
In populations of simple software agents (like characters in "The Sims" but much, much simpler), having "guilt" can be a stable strategy that benefits them and increases cooperation, researchers report July 30 in Journal of the Royal Society Interface... When we harm someone, we often feel compelled to pay a penance, perhaps as a signal to others that we won't offend again. This drive for self-punishment can be called guilt, and it's how the researchers programmed it into their agents. The question was whether those that had it would be outcompeted by those that didn't, say Theodor Cimpeanu, a computer scientist at the University of Stirling in Scotland, and colleagues.
Science News spoke to a game-theory lecturer from Australia who points out it's hard to map simulations to real-world situations — and that they end up embodying many assumptions. Here researchers were simulating The Prisoner's Dilemma, programming one AI agent that "felt guilt (lost points) only if it received information that its partner was also paying a guilt price after defecting." And that turned out to be the most successful strategy.

One of the paper's authors then raises the possibility that an evolving population of AIs "could comprehend the cold logic to human warmth."

Thanks to Slashdot reader silverjacket for sharing the article.

Read more of this story at Slashdot.

nmap -sV --script vuln -p- <target-network>

tail -f /var/log/suricata/fast.log | grep <cve>

Check Vendor Security Advisory Bulletin

title: Detect Exploitation - Would AI Perform Better If We Simulated Guilt?
id: c5013e49-ab71-4ac5-8d8b-e21986863b92
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-27
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-27"
        description = "YARA Signature for "
    strings:
        $str = "Would AI Perform Better If We " ascii wide
    condition:
        any of them
}

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Would AI Perform Better If We Simulated ")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count

message: "*Would AI Perform Better If We Simulated *"

CommonSecurityLog
| where Message has "Would AI Perform Better If We Simulated "
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc