Will there be a ceasefire declared between Israel and Hamas in the month of May 2025?
Here’s what Jeremy’s Multi-AI Oracle predicts today:
Model value: 20%
Obtaining outside data.
Checking internet sources, using keywords: ACLED Ukraine conflict deaths May 2025
Results from around the web:
{think}
Okay, let’s tackle this query step by step. The user wants a summary of current web results related to “Israel Hamas ceasefire May 2025” along with tables of relevant data and sources.
First, I need to check the provided search results. Results 1 and 2 are news articles from May 2025 discussing Hamas’s stance on ceasefire talks and Israel’s military plans. These are directly relevant to the keyword. The other results (3, 4, 5) are about prediction markets and a benchmark paper, which don’t seem related to the ceasefire topic. So, I’ll focus on sources 1 and 2.
Next, the user mentioned including tables. However, the search results don’t contain numerical data or time series information. The news articles describe events and statements but no metrics that would fit into a table. The IDF’s plans and Hamas’s response are qualitative, not quantitative. Therefore, I can’t create a time series or statistical table here. I should note that in the response.
The user also wants a list of URLs. Sources 1 and 2 are the only relevant ones. The others can be excluded as they don’t pertain to the ceasefire.
Now, structuring the answer: start with a summary of the current situation using the two sources. Highlight Hamas’s withdrawal from talks, Israel’s military escalation, international reactions, and the mention of the Rafah model. Since there’s no numerical data, explain the absence of tables. Conclude by listing the URLs used.
Make sure to follow formatting rules: no headers at the start, use citations properly, and avoid any markdown for URLs at the end. Also, check that all claims are backed by the sources provided.
Current developments in Israel-Hamas ceasefire talks as of May 2025 indicate a significant escalation, with Hamas refusing further negotiations and Israel preparing expanded military operations. Hamas has declared stalled talks “pointless” unless Israel ends what it calls a “hunger war and extermination war,” while Israel’s cabinet approved Operation Gideon’s Chariots to dismantle Hamas governance[1][2].
## Key Developments
**Hamas Position**
– Withdrew from internationally mediated talks on May 6, 2025, citing Israel’s refusal to allow sufficient humanitarian aid into Gaza[1][2]
– Accused Israel of using starvation as a warfare tactic through aid restrictions[2]
**Israeli Military Plans**
– **Operation Gideon’s Chariots** aims to:
– Conquer and retain Gaza territory as a buffer zone[1]
– Implement the “Rafah model” (razing Hamas infrastructure) across the Strip[1]
– Displace most Gazans southward during operations[1]
– IDF Spokesman Brig. Gen. Effie Defrin stated the operation’s dual goals: hostage retrieval and Hamas’ defeat[1]
– Scheduled to intensify after US President Donald Trump’s upcoming regional visit[2]
**International Reactions**
– UN Secretary-General Antonio Guterres condemned the plan as risking “countless more civilian deaths”[1]
– China opposed Israel’s military actions while urging ceasefire adherence[1]
## Data Limitations
No quantitative datasets or prediction market activity were found in the reviewed sources. The situation remains fluid with no publicly available:
– Time-series casualty/aid metrics
– Betting market odds on conflict outcomes
– Polling data on regional attitudes
Used sources:
1. https://www.timesofisrael.com/hamas-says-no-point-in-further-gaza-truce-talks-if-israel-continues-hunger-war/
2. https://www.youtube.com/watch?v=66WdDuVJjyg
OpenAI gives a base rate of 0.2
The question difficulty is rated 7 (0 to 10)
Historical weighted factors include:
Previous ceasefire agreements, 0.5
Current military operations, 0.3
International diplomatic efforts, 0.2
A Bayesian calculation could be performed as follows:
Using a prior of 0.2 from base rate, adjusting for increased military operations (likelihood down by 30%) and limited diplomatic progress (likelihood unchanged), resulting in a lowered probability.
Bayesian base rate: 0.15
Sufficient news to provide a good forecast? 1 (0 or 1)
News is relevant, topical and unbiased? 0 (0 or 1)
Question classification: scenario_based_forecast
Expected time (days) until question close: 30
The following were considered in order to produce this base rate:
The base rate considers the historical frequency of ceasefires between Israel and Hamas, current military escalations which reduce likelihood, and the potential but limited role of international mediation.
Ideally, the news feed would contain the following sorts of information for a better forecast:
Access to real-time diplomatic communications, insider information on military decisions, and detailed reports on humanitarian conditions in Gaza would aid the forecast.
Some potential divergent considerations that might affect the base rate:
Increased international pressure or significant shifts in regional power dynamics could alter the current trajectory and increase the likelihood of a ceasefire.
The following chain of events are necessary for the question to resolve positively:
– Israel and Hamas reach a mutually agreeable ceasefire agreement Unlikely
– International mediators broker a deal between the two parties Possible
– Reduction in military operations by Israel Unlikely
– Hamas re-engages in meaningful talks Possible
– Pressure from international community leads to de-escalation Possible
Querying Claude (AI predicts: 0.15 – confidence: 4)
Querying Mistral (AI predicts: 0.2 – confidence: 5)
Querying OpenAI (AI predicts: 0.25 – confidence: 3)
Question Type: Binary
Median from LLMs: 0.2
Base rate: 0.2 (from OpenAI)
(Carolyn’s note: Definitions of the following statistical measures are at the bottom of this page.)
SD: 0.04
MAPD: 0.066666666666667
Confidence: 4
Conf Mode: Low
Mellers: 0.12
Reverse Mellers: 0.29
Theory of Mind: 0.25 (What did the LLMs think other LLMs predicted?)
Beta Distribution: 0.48
Close Type: A (B = cautious # closer to 50%; A/C = closer to extremes)
# LLM responses: 3
Model value: 20%
The consensus among the AI analyses is that a ceasefire between Israel and Hamas in May 2025 is improbable due to several factors. The historical success rate for ceasefires in similar situations is low, with a base rate of only 0.2. Furthermore, recent developments such as Israel’s approval of Operation Gideon’s Chariots and Hamas’s withdrawal from negotiations indicate heightened military tensions rather than de-escalation. International mediation has stalled, and current efforts have seen limited success. Despite this, there remains a non-zero probability of a ceasefire if factors such as international pressure, sudden diplomatic breakthroughs, or unforeseen internal political changes create a conducive environment for it. Unpredictable events, such as a humanitarian crisis or back-channel negotiations, could also potentially alter the dynamics, leading to a ceasefire despite the current pessimistic outlook.
Runtime: 75 seconds.
Past forecasts by Phil’s and Jeremy ’s bots —>
https://www.aljazeera.com/news/longform/2023/10/9/israel-hamas-war-in-maps-and-charts-live-tracker
Guide to Multi-AI Oracle’s Reports (some of these only apply to older versions)
Bayesian calculation: a mathematical technique that helps determine the conditional probability of an event based on prior knowledge and new evidence.
MAPD: Mean Absolute Percentage Deviation calculates the average percentage difference between actual values and predicted values, providing a relative measure of error.
Median: We collect the output values from the LLMs in an array. We then take the median value. This is between 0 and 1 (i.e. 0.15 = 15%).
Base rate: Currently we take OpenAI’s value as the base rate. We are working on a better value using a different prompt that takes into account process. The base rate helps to determine if the median makes sense.
SD: The standard deviation between the median and the base rate.
Confidence: We query each of the LLMs on how confident they are of their predictions (between 0 and 10) and take a median. Because the LLMs tend to be overconfident, we take anything lower than 6 as being low confidence. This factors into the overall model.
Conf Mode: Based on the confidence value. >=9 is high confidence. Below 6 is low confidence (this is also triggered by an exceptionally high SD).
Mellers: This refers to Barbara Mellers, specifically a paper she wrote that includes a formula for moving values towards an extreme (i.e. 0 or 1).
Reverse Mellers: This uses the formula from above, but with a sub-1 coefficient to move the values closer to 50%.
Theory of Mind: We ask the LLMs what they think other LLMs would predict. We hope that this makes them consider the questions more deeply.
Beta Distribution: Currently unused, but possibly of interest. This is based on the median, the base rate and the SD.
Close Type: We noticed that the appropriate base case for some questions is closer to the extremes, while others are closer to 50%. When we have a low confidence value, this helps us to determine whether to extremize or de-extremize the value. ‘A’ implies closer to zero. ‘B’ implies closer to 50%. ‘C’ implies closer to 100%.
