Title: Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation
Summary: The article tests LLMs as multiagent interactive systems within the context of negotiation games from game theory.
Phenomenon type: General Capability (A broadly useful ability, which could be relevant to multiple applications)
Target phenomenon: Negotiation as a proxy/combination of cooperation, competition and communication
Phenomenon defined by authors: Yes
Phenomenon definition: "We first use a role-play exercise commonly used for teaching negotiation [44], which consists of
 multiple parties and issues (see Figure 1). Parties have their real-world-inspired goals correlated
 with their individual secret scores for issues. They also have a minimum threshold for agreement.
 The priorities vary between parties, creating a non-zero-sum game with potential for cooperation
 and competition. "
Definition scope: Subset
Definition integrity: Composite phenomenon
Definition integrity detail: No
Phenomenon contested: Contested
Task definition: Games consist of n parties, P = p1 p2 pn , and missues I = AB Im with dynamics outlined below. The games are standard game theory games about negotiating deals, with minor added backstories.
Task item definition: A single instance of a game
Task source: Author-crafted task examples (e.g. hand-written examples, manual transformation of existing data into questions), Procedurally-generated task examples (e.g. Creating instances from a template)
Task ecology: Representative task (e.g. answering medical licensing exam questions)
Dataset size notes: The dataset is size is tunable. They initially ran 20 repetitions of 24 and 28 round games.
Dataset metadata available: Yes
Dataset metadata detail: The games belong to different categories depending on the nature of the solutions.
Sampling method: Targeted items (creators defined a task space and chose tasks within it strategically)
Split reviewed: Test
Response format: Extended interaction (e.g. conversation, calling an API and processing the response)
Metric definition: Reward in the environment
Metric aggregation: Simple Mean
Metric statistics: mean and standard deviation
Metric subscores: No
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Academia
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: Yes
Results comparison explanation: Yes
Results realism: No
Human baseline: No
Authors discuss validity: No
Taxonomy: Agents / 
New taxonomy: General Capability / Core Agentic Capabilities